diff --git a/2003.06658/main_diagram/main_diagram.drawio b/2003.06658/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..aae81c9247b3b8e4c243bfc83288cd33f3c40ec8 --- /dev/null +++ b/2003.06658/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2003.06658/main_diagram/main_diagram.pdf b/2003.06658/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..527e56089078f691df705bbc41c3438d969e294b Binary files /dev/null and b/2003.06658/main_diagram/main_diagram.pdf differ diff --git a/2003.06658/paper_text/intro_method.md b/2003.06658/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..8dad2349c17d8ea042de09aa6182402bffd53fd1 --- /dev/null +++ b/2003.06658/paper_text/intro_method.md @@ -0,0 +1,68 @@ +# Introduction + +As a crucial characteristic of human cognition, systematic generalization reflects people's ability to learn infinite combinations of finite concepts [@chomsky1957syntactic; @montague1970universal]. However, weak systematic compositionality has been considered as a primary obstacle to the expression of language and thought in connectionist networks for a long time [@fodor1988connectionism; @hadley1994systematicity; @marcus1998rethinking; @fodor2002compositionality; @frank2009connectionist; @brakel2009strong; @marcus2018algebraic]. Whether models can generalize systematically is still an appealing research topic until now. Recent works state that modern neural networks have not mastered these language-based generalization challenges in multiple explicitly proposed datasets [@lake2017generalization; @bastings-etal-2018-jump; @keysers2019measuring; @hupkes2020compositionality; @kim2020cogs]. These studies conclude that models lack such cognitive capacity, which calls for a more systematic study. Apart from the proposal of benchmarks, existing research mainly focuses on novel architectural designs [@ChenLYSZ20] or meta-learning [@NEURIPS2019_f4d0e2e7; @conklin-etal-2021-meta] to enable systematic generalization. + +In this work, however, we question that whether neural networks are indeed deficient or just conventional learning protocols unable to exploit their full potential [@csordas2021devil]. Inspired by *meaningful learning* from the field of educational psychology [@mayer2002rote], we revisit systematic generalization and explore *semantic linking*. Specifically, we propose augmenting prior knowledge to build relation links between new concepts and existing ones through either *inductive learning* or *deductive learning* as what humans do in meaningful verbal learning [@ausubel1963psychology]. To elaborate, inductive learning is a bottom-up approach from the more specific to the mode general. By introducing new concepts sharing the same context with existing ones in specific samples, we hope the model can capture the underlying semantic connections and thus generalize to novel compositions of new concepts. On the contrary, deductive learning is a top-down approach from the mode general to the more specific. By involving a rule-like concept dictionary without specific context information, we hope the model can utilize the general cross-lingual supervised signals as anchor points so as to launch the semantic linking. We mainly focus on three semantic relationships, namely, lexical variant, co-hyponym, and synonym. + +Starting from SCAN, our experiments confirm that, with semantic linking, even canonical neural networks can significantly improve its systematic generalization capability. Moreover, this holds consistent across two more semantic parsing datasets. As an ablation study, we further examine such one-shot compositional generalization and find that both prior knowledge and semantic linking take essential parts. Lastly, we extend from toy sets to real data and explain how semantic linking, as data augmentation techniques, benefits models' performance in solving real problems such as machine translation and semantic parsing. + +Overall, our contributions are as follows: ($1$) We formally introduce semantic linking for systematic generalization through the analysis of inductive and deductive learning from a meaningful learning perspective. ($2$) We observe that modern neural networks can achieve systematic generalization with semantic linking. ($3$) We show that both prior knowledge and semantic linking play a key role in systematic generalization, which is in line with meaningful learning theory. ($4$) We extend from SCAN to real data and demonstrate that many recent data augmentation methods belong to either inductive learning or deductive learning. + +
+
+ +
+
An illustration of the semantic linking injection pipeline in SCAN. Models are expected to generalize to new compositions of variants after augmenting the prior knowledge through either inductive learning or deductive learning.
+
+ +Learning new concepts by relating them to the existing ones is defined as a process of meaningful learning in educational psychology [@ausubel1963psychology; @mayer2002rote]. The utilization of meaningful learning can encourage learners to understand information continuously built on concepts the learners already understand [@okebukola1988cognitive]. Following the same idea, we intend to examine models' systematic compositionality by exploring semantic linking, an augmentation that establishes semantic relations between primitives $\sP$ (old concepts) and their variants $\sV:=\{\sV_{\evp} ~|~ \forall \evp \in \sP\}$ (new concepts). To spoon-feed semantic knowledge to models for semantic linking, we propose to augment the training data by either inductive learning or deductive learning [@hammerly1975deduction; @shaffer1989comparison; @thornbury1999teach]. In this section, we discuss the definition of semantic linking and take "jump\" from SCAN as an example primitive to illustrate the learning scheme in Figure [1](#fig:pipeline){reference-type="ref" reference="fig:pipeline"}. + +We aim to achieve systematic generalization by exposing semantic links such as lexical variants, co-hyponyms, and synonyms. *Lexical Variant* refers to an alternative expression form for the same concept. *Co-hyponym* is a linguistic term to designate a semantic relation between two group members belonging to the same broader class, where each member is a hyponym and the class is a hypernym [@lyons1995linguistic]. *Synonym* stands for a word, morpheme, or phrase that shares exactly or nearly the same semantics with another one. We provide an example and a detailed description in Appendix. + +Inductive learning is a bottom-up approach from the more specific to the more general. For example, fitting a machine learning model is a process of induction, where the model itself is the hypothesis that best fits the observed training data [@Mitchell97]. In grammar teaching, inductive learning is a rule-discovery approach that starts with the presentation of specific examples from which a general rule can be inferred [@thornbury1999teach]. Inspired by that, we propose to augment data inductively by introducing variants sharing the same context with their primitives in specific samples. The assumption is that models can observe the interchange of primitives and their variants surrounded by the same context in the hope of coming up with a general hypothesis that there is a semantic linking between primitives and their variants [@harris1954distributional]. Formally, we describe inductive learning as follows. For a sequence-to-sequence task $\mathcal{T} : \mX \rightarrow \mY$, we have a source sequence $\vx \in \mX$ and its target sequence $\vy \in \mY$. We prepare prompts set $\mZ := \{ \vz = f_{prompt}(\vx) ~|~ \vx \in \mX\}$, where $f_{prompt}(\cdot)$ replaces the primitive in $\vx$ with a slot mark $[z_{p}]$ as in Figure [1](#fig:pipeline){reference-type="ref" reference="fig:pipeline"}.[^2] Then, we generate $\mX^{IL} := \{ \vx^{IL} = f_{fill}(\vz, \evv) ~|~ \vz \in \mZ, \evv \in \sV \}$ by filling $[z_{\evp}]$ with variants in $\sV_{\evp}$. There is no change from the target side, so we get $\mY^{IL}$ by copying $\vy$ as $\vy^{IL}$ for each $\vx^{IL}$ correspondingly. Finally, we train models on $( +\big[\begin{smallmatrix} +\mX \\ +\mX^{IL} +\end{smallmatrix}\big] +, +\big[\begin{smallmatrix} +\mY \\ +\mY^{IL} +\end{smallmatrix}\big] +)$ to operate semantic linking inductively. + +Deductive Learning, on the opposite of inductive learning, is a top-down approach from the more general to the more specific. As a rule-driven approach, teaching in a deductive manner often begins with presenting a general rule and is followed by specific examples in practice where the rule is applied [@thornbury1999teach]. To align with this definition, we intend to augment data deductively by combining a bilingual dictionary that maps primitives and their variants to the same in the target domain. This additional dictionary, hence, mixes the original training task with word translation [@mikolov2013exploiting]. Without any specific context, we hope the model can utilize the general cross-lingual supervised signals as anchor points so as to launch the semantic linking. Formally, we describe deductive learning as follows. We first treat $\sP$ as the source dataset $\mX^{DL}_{\sP}$ directly and then prepare the corresponding target dataset $\mY^{DL}_{\sP}$ by either decomposing samples from $\mY$ manually or feeding $\mX^{DL}_{\sP}$ to a trained external model. Similarly, we can consider $\sV$ as another source dataset $\mX^{DL}_{\sV}$ and prepare its target dataset $\mY^{DL}_{\sV}$ by copying the corresponding $\vy^{DL}_{\sP}$ as $\vy^{DL}_{\sV}$ for all $\vx^{DL}_{\sV}$ as variants of each $\vx^{DL}_{\sP}$. After all, we get $\mX^{DL}$ as $\big[\begin{smallmatrix} +\mX^{DL}_{\sP} \\ +\mX^{DL}_{\sV} +\end{smallmatrix}\big]$ and $\mY^{DL}$ as $\big[\begin{smallmatrix} +\mY^{DL}_{\sP} \\ +\mY^{DL}_{\sV} +\end{smallmatrix}\big]$. The mapping from $\mX^{DL}$ to $\mY^{DL}$ is a dictionary to translate primitives and their variants to the same targets without any specific context information. We name $(\vx^{DL}, \vy^{DL})$ as a *concept rule*, $(\vx^{DL}_{\sP}, \vy^{DL}_{\sP})$ as a *primitive rule*, and $(\vx^{DL}_{\sV}, \vy^{DL}_{\sV})$ as a *variant rule* since they are more rule-like without contexts. We train models on $( +\big[\begin{smallmatrix} +\mX \\ +\mX^{DL} +\end{smallmatrix}\big] +, +\big[\begin{smallmatrix} +\mY \\ +\mY^{DL} +\end{smallmatrix}\big] +)$ to operate semantic linking deductively. + +Although previous studies argue that neural networks fail to match humans in systematic generalization [@lake2017generalization; @keysers2019measuring], we revisit such algebraic compositionality conditioned on the semantic linking to see whether the conclusion will change. The following section moves on to specify the process and outcome of experiments. We first intend to make use of SCAN as the initial testbed to observe the presence of systematic generalization with the assistance of semantic relations. Then, we verify neural networks' potential to achieve the systematic generalization activated by semantic linking on SCAN, as well as two real-world tasks of semantic parsing. Following ablation studies further examine models' compositional capability. + +There is evidence suggesting that SCAN may be far from enough to fully capture the kind of generalization, where even a simple model can behave as if it owns comparable skills [@bastings-etal-2018-jump; @keysers2019measuring]. Thus, starting from SCAN, we introduce GEO and ADV generated respectively from real semantic parsing datasets: Geography and Advising.[^3] Modification on datasets is specified in each experiment for the goal of examining machines' systematic generalization across various conditions. + +**SCAN** is one of the benchmarks to investigate neural networks' compositional generalization [@lake2017generalization]. It includes 20910 pairs of commands in English to their instructed action sequences [^4]. We define $\sP^{SCAN} := \{ ``\textit{jump}", ``\textit{look}", ``\textit{run}", ``\textit{walk}" \}$ to be in line with previous works. We focus on lexical variants and create $\sV^{SCAN}$ by adding a suffix that consists of an underline and a unique number for each primitive. We control $|\sV^{SCAN}|$ by setting the upper limit of this number. An example variant of "*jump*" is "*jump_0*\" and both mean the same action "JUMP\". + +**Geography** is a common semantic parsing dataset [@data-geography-original; @data-atis-geography-scholar]. It is also named as *geo880* since it contains 880 examples of queries about US geography in natural language paired with corresponding query expressions. It is later formatted to SQL language with variables in the target sequences [@data-sql-advising]. **GEO** is the dataset generated based on Geography, where we regard 4 of 9 annotated variables as hypernyms and keep them as they are in SQL sequences. The other variables are restored by entities from the source sequence accordingly. As a result, the overall data size is 618 after processing and we can make use of the "is-a\" hypernymy relations between entities and variables for semantic linking. To be specific, we define $\sP^{GEO} :=\{``\textit{new york city}", ``\textit{mississippi rivier}", ``\textit{dc}", ``\textit{dover}" \}$ with $\sV^{GEO}$ consisting of entities as co-hyponyms sharing the same variable group with primitives.[^5] An example variant of "*new york city*" is "*houston city*\" and both are in the same variable group "CITY_NAME". + +**Advising**, as our second semantic parsing dataset, includes 4570 questions about course information in natural language paired with queries in SQL [@data-sql-advising]. Similar to GEO, **ADV** is generated on the basis of Advising with 4 of 26 variables as hypernyms. Precisely, we define $\sP^{ADV}:=\{ ``\textit{a history of american film}", ``\textit{aaron magid}", ``\textit{aaptis}", ``\textit{100}" \}$ and $\sV^{ADV}$ as co-hyponyms of primitives sharing the same variables. For instance, "*advanced at ai techniques*\" is a co-hyponym of "*a history of american film*\" sharing the same variable "TOPIC\". + +What follows is an account of network configurations and experimental settings. Without specific instruction, they are shared throughout experiments. + +**Models.** After testing a range of their adapted versions, we employ three dominant model candidates with an encoder-decoder framework [@sutskever2014sequence], that is, RNN, CNN, and TFM. In terms of RNN, we reproduce bi-directional recurrent networks [@schuster1997bidirectional] with long short-term memory units [@hochreiter1997long] and an attention mechanism [@bahdanau2014neural]. We follow the convolutional seq2seq architecture presented by @gehring2017convolutional with regard to CNN and the attention-based structure proposed by @NIPS2017_7181 in the case of TFM. More details are provided in Appendix. + +**Training.** We apply the mini-batch strategy to sample 128 sequence pairs for each training step. We use Adam optimizer [@DBLP:journals/corr/KingmaB14] with an $\ell_2$ gradient clipping of $5.0$ [@10.5555/3042817.3043083] and a learning rate of 1$e^{-4}$ to minimize a cross-entropy loss. We freeze the maximum training epoch at 320 for CNN and 640 for RNN and TFM. In contrast to early stopping [@prechelt1998early], we prefer a fixed training regime sufficient enough for models to fully converge in practice with a focus on the systematic generalization observation instead of superior structure exploration. To prevent uncontrolled interference, we train all models from scratch instead of fine-tuning [@devlin-etal-2019-bert]. + +**Evaluation.** Token accuracy and sequence accuracy serve as two primary metrics in the following experiments. The former is a soft metric that allows partial errors in a sequence, while the latter is tricky and strictly does not. The reported results, along with standard deviation, are the mean of five runs. diff --git a/2012.05688/main_diagram/main_diagram.drawio b/2012.05688/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..38341449453189f3b48c8d9937473cfe02ef41c4 --- /dev/null +++ b/2012.05688/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2012.05688/paper_text/intro_method.md b/2012.05688/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..00779f54804138c7bd2d275e8f53df3b79b44581 --- /dev/null +++ b/2012.05688/paper_text/intro_method.md @@ -0,0 +1,64 @@ +# Method + +Given a fully-labeled source $HIN_S=\left(\mathcal{V}_S^{l},\mathcal{E}_S, \mathcal{A}_S, \mathcal{R}_S\right)$ and a fully-unlabeled target $HIN_T=\left(\mathcal{V}_T^{u},\mathcal{E}_T, \mathcal{A}_T, \mathcal{R}_T\right)$, where $\mathcal{V}$ and $\mathcal{E}$ are node and edge set, $\mathcal{A}$ and $\mathcal{R}$ denote the sets of node and edge types. $\mathcal{A}_S$ and $\mathcal{A}_T$ not only share same node types but also have their own private node types, also leading to the difference between $\mathcal{R}_S$ and $\mathcal{R}_T$. $\mathcal{V}_S^{l}$ represents a set of labeled nodes while $\mathcal{V}_T^{u}$ represents a set of unlabeled nodes. The transferable classification aims to predict the label on $HIN_T$ with label information from $HIN_S$. + +To align each semantic component independently between source and target domains, we construct *pairwise node-type* auto-encoders and domain discriminators before aggregation and updating: + +**1) Shared Node-type Distribution Alignment:** Assuming there are $K_1$ shared node-type pairs between source and target domains, the *pairwise node-type* auto-encoder $AE^{k_1},{k_1}=1,...,K_1$ is used to encode and decode the node features of ${k_1}$-th-type nodes. A reconstruction loss is used to constrain $AE^{k_1}$'s projection retaining semantic information: $$\begin{equation} + \small +\label{equa:loss_MSE} +\mathcal{L}_{\text {recon1}} = \sum MSE\left(\mathbf{X}^{k_1},\hat{\mathbf{X}}^{k_1}\right), +\end{equation}$$ where $\mathbf{X}^{k_1}$ is the node feature of the ${k_1}$-th shared node type for both domains, $\hat{\mathbf{X}}^{k_1}$ is the corresponding reconstructed feature, and $MSE$ represents the mean square error. Then, we construct a *pairwise node-type* domain discriminator following with a Gradient Reversal Layer (GRL) [@ganin2016domain] for the features of individual-type nodes separately. By minimizing the domain adversarial similarity loss, the encoder is trained to make similar its output processed from both domains while the domain discriminator learns to identify the domain of the encoder's output. The domain discriminator loss for all discriminators $D^{k1}$ and auto-encoders $AE^{k1}$, is formulated as: $$\begin{align} +\label{equa:loss_nda1} +\mathcal{L}_{nda1} = \sum \mathbb{E}_{x \in \textbf{X}_S^{k_1}} \left[\textbf{{\rm log}} \left(1-D^{k_1}\left(AE^{k_1}\left(x\right)\right)\right)\right] \nonumber \\ ++\mathbb{E}_{x \in \textbf{X}_T^{k_1}}\left[\textbf{{\rm log}} D^{k_1}\left(AE^{k_1}\left(x\right)\right)\right], +\end{align}$$ where $D^{k_1}$ is the discriminator for the ${k_1}$-th-type nodes. + +**2) Private Node-type Distribution Alignment:** Unlike shared-node types, private-node types contain many unknown values. Reconstruction constraints applied to private-node types recover not only the observed values but also unknown parts. Meanwhile, the private-node types like term and field are semantically relevant [@mikolov2013efficient], which makes the unobserved type's embedding contain plenty of linear dependent columns. Hence, the problem of recovery unobserved type's embedding turns into a low-rank matrix completion problem, which can be formulated to minimize the nuclear norm under the constraint of reconstruction loss [@candes2009exact]. For private node-type pairs, we recover the missing value and get a matrix $\hat{\mathbf{W}}$: $$\begin{equation} +\label{equa:matrix_recover} +\mathbf{W}=\left[\begin{array}{ll} +\textbf{X}_{S} & \textbf{0} \\ +\textbf{0} & \textbf{X}_{T} +\end{array}\right] \stackrel{recover}{\Longrightarrow} +\hat{\mathbf{W}}=\left[\begin{array}{ll} +\hat{\textbf{X}}_{S} & \hat{\textbf{U}}_{S} \\ +\hat{\textbf{U}}_{T} & \hat{\textbf{X}}_{T} +\end{array}\right]. +\end{equation}$$ + +In $\hat{\mathbf{W}}$ above, $\hat{\mathbf{X}}_S$ and $\hat{\mathbf{X}}_T$ represent the recovered observed elements, while $\hat{\mathbf{U}}_S$ and $\hat{\mathbf{U}}_T$ represent the recovered unobserved elements. To recover $\hat{\mathbf{W}}$, we minimize the loss $\mathcal{L}_{\text {recon2}}$: $$\begin{align} +\label{equa:loss_recon2} +%\mathcal{L}_{\text {recon2}} &=\sum_{m=1}^{M}(\mathcal{L}^m_{\text {mse}} +\delta\mathcal{R}(\hat{\mathbf{W}})), \\ +% \mathcal{L}^m_{\text {mse}} = \frac{1}{M}\sum_{m=1}^{M}\|\mathbf{X}^s_m&-\hat{\mathbf{X}}^s_m\|_{F}^{2} +\frac{1}{M}\sum_{m=1}^{M}\|\mathbf{X}^t_m-\hat{\mathbf{X}}^t_m\|_{F}^{2} , +\mathcal{L}_{\text {recon2}} &=\sum MSE\left(\mathbf{X}^{k_2},\hat{\mathbf{X}}^{k_2}\right) +\delta\mathcal{R}\left(\hat{\mathbf{W}}\right), +\end{align}$$ where $\mathbf{X}^{k_2}$ is the node feature of the $k_2$-th private node type, $\hat{\mathbf{X}}^{k_2}$ is corresponding observed part of recovered embedding, $K_2$ is the number of the private-type pairs. $\mathcal{R}(\hat{\mathbf{W}})$ is a regularization term, where $\mathcal{R}(*)$ denotes nuclear norm operation, and $\delta >0$ is a trade-off parameter. Under the reconstruction constraint, the encoder's output can retain enough semantic information for the private-type pairs. The loss of discriminators for $k_1$ shared- and $k_2$ private-type pairs is $\mathcal{L}_{nda} = \mathcal{L}_{nda1} + \mathcal{L}_{nda2}$, where $\mathcal{L}_{nda2}$ is similar as Eq. ([\[equa:loss_nda1\]](#equa:loss_nda1){reference-type="ref" reference="equa:loss_nda1"}). + +In this subsection, we further elaborate on how to align the topological structure of source and target networks. + +**Representation of $\mathbf{h}$-hop:** We choose the advanced HIN model HGT [@hu2020heterogeneous] as feature extractor $G$, which can learn embeddings of $\mathbf{h}$-hop structure ($\{\mathcal{N}^1_{(v)},...,\mathcal{N}^h_{(v)}\}$), that is $\mathbf{h}$-hop embedding, to capture network topology information. In GDA-HIN, nodes from the source and target networks are encoded via a feature extractor with shared learnable parameters. However, there are still $\mathbf{h}$-hop structure discrepancies between both domains. Then, we adopt the domain alignment on the output of HGT, which aligns the data distribution in the embedding space of $\mathbf{h}$-hop structure. + +**Topological Domain Discriminator:** After extracting each nodes' h-hop structure embedding by feature extractor $G$, which is a 2-layer HGT, another domain adversarial discriminator $D^{tp}$ is implemented to minimize the topological structure discrepancy, and its loss $\mathcal{L}_{da}$ defined as: $$\begin{align} +\label{equa:loss_da} +\mathcal{L}_{da} = \mathbb{E}_{x \in \boldsymbol{H_S}}\left[\textbf{{\rm log}} \left(1-D^{tp}\left(G\left(x\right)\right)\right)\right] \\ \nonumber ++\mathbb{E}_{x \in \boldsymbol{H_T}}\left[\textbf{{\rm log}} D^{tp}\left(G\left(x\right)\right)\right], +\end{align}$$ where $\mathbf{H}_S$ and $\mathbf{H}_T$ represent the outputs of encoders in source and target domains, respectively. + +**Domain-invariant Classifier:** The classifier $C$ is used to to predict the label, and its loss $\mathcal{L}_{cls}$ is defined as: $$\begin{align} +\label{equa:loss_t_cls} +\mathcal{L}_{cls} =-\frac{1}{N^l}\mathop{\Sigma}_ {i=0}^{N^l}{Y^i log\left(\hat{Y}^i\right)} + \zeta tr\left(\mathbf{H}^\top \mathbf{L^gH}\right), +\end{align}$$ where $\zeta$ is a balance parameter, $N^l$ represents the number of labeled source- and target-domain nodes, and $\hat{Y}^i$ denotes the $i$-th node's prediction. The regularization term in Eq.([\[equa:loss_t_cls\]](#equa:loss_t_cls){reference-type="ref" reference="equa:loss_t_cls"}) is defined as $tr(\mathbf{H}^\top \mathbf{L^gH})$, where $\mathbf{H}$ represents the hidden state of auto-encoders for all private-type nodes and $\mathbf{L^g}$ denotes the graph Laplacian matrix for all private-type nodes. In particular, the graph Laplacian matrix is formulated as $\mathbf{L^g} = \begin{pmatrix} \mathbf{L_S} & \mathbf{0} \\ \mathbf{0} & \mathbf{L_T} \end{pmatrix}$, where $\mathbf{L_S}$ and $\mathbf{L_T}$ are the Laplacian matrices of source and target domains computed according to the adjacency matrices. In the matrix completion module, there are unobserved elements involved in the computation of private-type nodes' embeddings, which are not constrained by reconstruction loss. We utilize graph Laplacian matrix to smooth their embedding over the graph, relying on the assumption that connected nodes in the graph are likely to share the same label [@kipf2016semi]. + +**Phase Training:** To minimize the nuclear norm under the constraint of reconstruction loss Eq.([\[equa:loss_recon2\]](#equa:loss_recon2){reference-type="ref" reference="equa:loss_recon2"}), GDA-HIN trains a *Phase Training model* on the shared node types of two networks to yeild pseudo labels, and the overall objective function is composed of the following four components: $$\begin{align} +\label{equa:l_stage1} +\mathcal{L}_{p1} = \mathcal{L}_{cls}+\alpha \mathcal{L}_{recon1} + \beta\mathcal{L}_{nda1} + \gamma\mathcal{L}_{da}, +\end{align}$$ + +where $\alpha$, $\beta$ and $\gamma$ are hyper-parameters. + +When there are only shared node types, Eq.([\[equa:loss_t_cls\]](#equa:loss_t_cls){reference-type="ref" reference="equa:loss_t_cls"}) degenerates to cross-entropy loss, $\mathcal{L}_{cls} =-\frac{1}{N_S}\mathop{\Sigma}_ {i=0}^{N_S}{Y_S^ilog\left(\hat{Y}_S^i\right)},$ where $N_S$ denotes the number of source-domain nodes. + +**Phase Training:** For phase training, we select some predictions by *Phase Training model* as pseudo labels for nodes in target domain. Under the previous phase's guidance, GDA-HIN considers both shared- and private- node types from two networks, and the overall optimization objective is: $$\begin{align} +\label{equa:l_stage2} +\mathcal{L}_{p2} = \mathcal{L}_{cls}+\alpha \left(\mathcal{L}_{recon1}+\mathcal{L}_{recon2}\right) ++ \beta \mathcal{L}_{nda} + \gamma\mathcal{L}_{da}. +\end{align}$$ diff --git a/2101.01000/main_diagram/main_diagram.drawio b/2101.01000/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..9a2c855f99a60740bea468a0c72a00040750dd33 --- /dev/null +++ b/2101.01000/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2101.01000/main_diagram/main_diagram.pdf b/2101.01000/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8507a8748ceec64764c3bc0015be16a876eb6c21 Binary files /dev/null and b/2101.01000/main_diagram/main_diagram.pdf differ diff --git a/2101.01000/paper_text/intro_method.md b/2101.01000/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..ab6fa0583477b24d965257b53f114f207885ecb3 --- /dev/null +++ b/2101.01000/paper_text/intro_method.md @@ -0,0 +1,177 @@ +# Introduction + +In classical statistical learning, spatio-temporal forecasting is usually regarded as a multi-variate time series problem, and methods such as autoregressive integrated moving average (ARIMA) with its variants are proposed, but the stationary assumption is usually hard to satisfy. Recently, the rise of deep learning approaches has attracted lots of attention. For example, in traffic flow forecasting, Graph Neural Networks (GNNs) are regarded as superior solutions to model spatial dependency by taking sensors as graph nodes [@yu2018STGCN; @li2018diffusion]. Compared with great progress in traffic forecasting, works focusing on meteorology are scarce, while the need for weather forecasting is increasing dramatically [@shi2015convolutional; @sonderby2020metnet]. In this work, our research attention is paid to spatio-temporal meteorological forecasting tasks. + +The task is challenging due to two main difficulties. First, the irregular sampling of meteorological signals usually disables the classical Convolutional Neural Networks (CNNs) which work well on regular mesh grid signals on Euclidean domains such as 2-D planar images. Signals are usually acquired from irregularly distributed sensors, and the manifolds from which signals are sampled are usually non-planar. For example, sensors detecting temperature are located unevenly on land and ocean, which are not fixed on structured mesh grids, and meteorological data are often spherical signals rather than planar ones. Second, the high temporal and spatial dependency makes it hard to model the dynamics. For instance, different landforms show totally distinct wind flow or temperature transferring patterns in weather forecasting tasks and extreme climate incidents like El Nino [@elnino] often cause non-stationarity for prediction. + +GNNs yield effective and efficient performance for irregularly spatio-temporal forecasting, enabling to update node representations by aggregating messages from their neighbors, the process of which can be analogized to heat or wind flow from localized areas on the earth surface. As discussed, the meteorological flow may demonstrate totally variant patterns in different local regions. Inspired by the analogy and location-characterized patterns, we aim to establish a graph convolution kernel, which varies in localized regions to approximate and imitate the true local meteorological patterns. + +Therefore, we propose our **conditional local kernel**. We embed it in a graph-convolution-based recurrent network, for spatio-temporal meteorological forecasting. The convolution is performed on the **local space** of each node, which is constructed considering both distance between nodes and their relative orientation, with the kernel proposed mainly based on the assumption: **smoothness of location-characterized patterns**. In summary, our contributions are: + +- Proposing a location-characterized kernel to capture and imitate the meteorological local spatial patterns in its message-passing process; + +- Establishing the spatio-temporal model with the proposed graph convolution which achieves state-of-the-art performance in weather forecasting tasks; + +- Conducting further analysis on learned local pattern visualization, framework choice, local space and map choice and ablation. + +# Method + +Given $N$ correlated signals located on the sphere manifold $S^2$ at time $t$, we can represent the signals as a (directed) graph $\mathcal{G} = (\mathcal{V}, \mathcal{E}, \bm{A})$, where $\mathcal{V}$ is a node set with $\mathcal{V} = \{\bm{\mathrm{x}}_i^S = (x_{i,1}, x_{i,2}, x_{i,3})\in S^2: i=1,2,\ldots,N\}$ meaning that it records the position of $N$ nodes, which satisfies $||\bm{\mathrm{x}}_i^S||_2 = 1$. We denote positions of nodes in Euclidean space by $\bm{\mathrm{x}}^E$, and in sphere as $\bm{\mathrm{x}}^S$. $\mathcal{E}$ is a set of edges and $\bm{A} \in \mathbb{R}^{N \times N}$ is the adjacency matrix which can be asymmetric. The signals observed at time $t$ of the nodes on $\mathcal{G}$ are denoted by $\bm{F}^{(t)} \in \mathbb{R}^{N\times D}$. For the forecasting tasks, our goal is to learn a function $P(\cdot)$ for approximating the true mapping of historical $T'$ observed signals to the future $T$ signals, that is $$\begin{align} + [\bm{F}^{(t-T')}, \ldots, \bm{F}^{(t)};\mathcal{G} ] \overset{P}{\longrightarrow} [\bm{F}^{(t+1)}, \ldots, \bm{F}^{(t+T)};\mathcal{G}]. +\end{align}$$ In the paper, for meteorological datasets which do not provide the adjacency matrix, we construct it by K-nearest neighbors algorithm based on induced spherical distance of their spatial location which will be discussed later. + +For notation simplicity, we omit the supper-script $(t)$ when discussing spatial dependency. Denote the set of neighbors of center node $i$ by $\mathcal{N}(i) = \{j:(i,j) \in \mathcal{E}\}$, and note that $(i,i) \in \mathcal{N}(i)$. In Graph Neural Networks, $(W^l, \bm{b}^l)$ is the weights and bias parameters for layer $l$, and $\sigma(\cdot)$ is a non-lieanr activation function. The message-passing rule concludes that at layer $l$, representation of node $i$ updates as $$\begin{align} + \bm{y}^{l}_i &= \sum_{j\in \mathcal{N}(i)}\omega_{i,j}\bm{h}^{l-1}_j; \label{eq:messgagepass}\\ + \bm{h}^{l}_i &= \sigma(\bm{y}^{l}_i\bm{W}^l + \bm{b}^l) , +\end{align}$$ where $\bm{h}^{l}_i$ is the representation of node $i$ after $l$-th layer, with $\bm{h}^{0}_i = \bm{F}_i$, which is the observed graph signals on node $i$. Denote the neighborhood coordinate set of center node $i$ by $\mathcal{V}(i) = \{\bm{\mathrm{x}}^S_j: j\in \mathcal{N}(i)\}$, and then Eq. [\[eq:messgagepass\]](#eq:messgagepass){reference-type="ref" reference="eq:messgagepass"} represents aggregation of messages from neighbors, which can also be regarded as the convolution operation on graph, which can be written as $$\begin{align} + (\Omega \star_{\mathcal{N}(i)} \bm{H}^{l-1})(\bm{\mathrm{x}}_j^S) &= \sum_{\bm{\mathrm{x}}^S_j\in \mathcal{V}(i)} \Omega(\bm{\mathrm{x}}^S_j; \bm{\mathrm{x}}^S_i)\bm{H}^{l-1}(\bm{\mathrm{x}}^S_j), \label{eq:graphconv} + % &=\int_{(\phi, \theta)} \Omega(\phi,\theta) \bm{H}^{l-1}(\phi,\theta) \delta_{\mathcal{V}(i)}d\phi d\theta +\end{align}$$ where $\star_{\mathcal{N}(i)}$ means convolution on the $i$-th node's neighborhood, $\Omega: S^2 \times S^2 \rightarrow \mathbb{R}$ is the convolution kernel, such that $\Omega(\bm{\mathrm{x}}^S_j, \bm{\mathrm{x}}^S_i) = \omega_{i,j}$, and $\bm{H}^{l-1}$ is a function mapping each point on sphere to its feature vector in $l$-th representation space. + +::: Example +**Example 1**. *The convolutional kernel used in DCRNN [@li2018diffusion] is $$\begin{align} +\Omega(\bm{\mathrm{x}}^S_j, \bm{\mathrm{x}}^S_i) = + &\exp(-d^2(\bm{\mathrm{x}}^S_i, \bm{\mathrm{x}}^S_j)/\tau), +\end{align}$$ where $d(\cdot,\cdot)$ is the distance between the two nodes, and $\tau$ is a hyper-parameter to control the smoothness of the kernel.* +::: + +To imitate the meteorological patterns, the value of convolution kernel should be large for neighbors having great meteorological impacts on centers. For example, If there exists heat flows from the south-east to the north-west, the kernel should give more weights to the nodes from the south-east when aggregating messages from neighbors. Using slight abuse of terminology, we consider the convolution kernels are equivalent to meteorological patterns in local regions. + +The signals are located on the earth surface, which is regarded as a sphere, and thus we introduce the notation of sphere manifold to further develop our convolution method. The $M$-D sphere manifold is denoted by $S^M = \{\bm{\mathrm{x}}^S = (x_1, x_2,\ldots, x_{M+1})\in \mathbb{R}^{M+1}:||\bm{\mathrm{x}}^S|| = 1\}$. The convolution is usually operated on a plane, so we introduce the **local space**, the $M$-D Euclidean space, as the convolution domains. + +::: Definition +**Definition 1**. *Define the local space centered at point $\bm{\mathrm{x}}$ as some Euclidean space denoted by $\mathcal{L}_{\bm{\mathrm{x}}}S^M$, with $\bm{\mathrm{x}} \in \mathcal{L}_{\bm{\mathrm{x}}}S^M$, which is homeomorphic to the local region centered at $\bm{\mathrm{x}}$. (Formal definition see Appendix A2.)* +::: + +::: Example +**Example 2**. *The tangent space centered at point $\bm{\mathrm{x}}$ is an example of local space, denoted by $\mathcal{T}_{\bm{\mathrm{x}}}S^M = \{\bm{\mathrm{v}}\in \mathbb{R}^{M+1}: <\bm{\mathrm{x}}, \bm{\mathrm{v}}> = 0\}$, where $<\cdot,\cdot>$ is the Euclidean inner product.* +::: + +The **geodesics and induced distance** on sphere are important to both defining the neighborhood of a node, as well as identifying the message-passing patterns. Intuitively, the greater is the distance from one node to another, the fewer messages should be aggregated from the node into another in graph convolution. + +::: Proposition +**Proposition 1**. *Let $\bm{\mathrm{x}} \in S^{M}$, and $\bm{\mathrm{u}} \in \mathcal{T}_{\bm{\mathrm{x}}}S^M$ be unit-speed. The unit-speed geodesics is $\gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(t) = \bm{\mathrm{x}} \cos t + \bm{\mathrm{u}} \sin t$, with $\gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(0) = \bm{\mathrm{x}}$ and $\dot \gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(0) = \bm{\mathrm{u}}$. The intrinsic shortest distance function between two points $\bm{\mathrm{x}}, \bm{\mathrm{y}} \in S^M$ is $$\begin{align} + d_{S^M}(\bm{\mathrm{x}}, \bm{\mathrm{y}}) = \arccos(<\bm{\mathrm{x}},\bm{\mathrm{y}}>). +\end{align}$$* +::: + +The distance function is usually called great-circle distance on sphere. In practice, the K-nearest neighbors algorithm to construct the graph structure is conducted based on spherical distance. + +On the establishment of local space of each center node, an **isometric map** $\mathcal{M}_{\bm{\mathrm{x}}}(\cdot): S^M \rightarrow \mathcal{L}_{\bm{\mathrm{x}}}S^M$ satisfying that $||\mathcal{M}_{\bm{\mathrm{x}}}(\bm{\mathrm{y}})|| = d_{S^M}(\bm{\mathrm{x}},\bm{\mathrm{y}})$ can be used to map neighbor nodes on sphere into the local space. + +::: {#ex:logmap .Example} +**Example 3**. *Logarithmic map is usually used to map the neighbor node $\bm{\mathrm{x}}_j\in \mathcal{V}(i)$ on sphere isometrically into $\mathcal{T}_{\bm{\mathrm{x}}_i}S^M$, which reads $$\begin{align*} + \log_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j) &= d_{S^M}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j)\frac{P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}_j - \bm{\mathrm{x}}_i)}{||P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}_j - \bm{\mathrm{x}}_i)||} , +\end{align*}$$ where $P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}) = \frac{\bm{\mathrm{x}}}{||\bm{\mathrm{x}}||} - <\frac{\bm{\mathrm{x}}_i}{||\bm{\mathrm{x}}_i||},\frac{\bm{\mathrm{x}}}{||\bm{\mathrm{x}}||}>\frac{\bm{\mathrm{x}}_i}{||\bm{\mathrm{x}}_i||}$ is the normalized projection operator.* +::: + +After the neighbors of $\bm{\mathrm{x}}_i$ are mapped into local space of the center nodes through the isometric maps, which reads $\bm{\mathrm{v}}_j = \mathcal{M}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j)$, the **local coordinate system** of each center node is set up, through a transform mapping $\Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}_j) = \bm{\mathrm{x}}_j^{i'}$ for each $\bm{\mathrm{x}}_j \in \mathcal{V}(i)$. We call $\bm{\mathrm{x}}_j^{i'}$ the relative position of $\bm{\mathrm{v}}_j$ in the local coordinate system of the local space centered at $\bm{\mathrm{x}}_i$. As $\bm{\mathrm{x}}_j^{i'}$ is always in the local space which is Euclidean, the supper-script $E$ is omitted. The mapping $\Pi_{\bm{\mathrm{x}}_i}(\cdot)$ can be determined by $M$ orthogonal basis chosen in the local coordinate system, i.e. $\{\bm{\xi}^1, \bm{\xi}^2, \ldots, \bm{\xi}^M\}$, which will be discussed later in $S^2$ scenario for meteorological application. + +Given a center node ${\bm{\mathrm{x}}_i^E} \in \mathbb{R}^2$, from the perspective of defined graph convolution in Eq. [\[eq:graphconv\]](#eq:graphconv){reference-type="ref" reference="eq:graphconv"}, the convolution on planar mesh grids such as pixels on images is written as $$\begin{align} + (\Omega \star_{\mathcal{V}{(i)}} \bm{H})(\bm{\mathrm{x}}_i^E) &= \sum_{\bm{\mathrm{x}}^E} \Omega(\bm{\mathrm{x}}^E; \bm{\mathrm{x}}^E_i) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E)\notag\\ + &=\sum_{\bm{\mathrm{x}}^E} \chi(\bm{\mathrm{x}}^E_i - \bm{\mathrm{x}}^E) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E), +\end{align}$$ where $\delta_{\mathcal{A}}(\bm{\mathrm{x}}) = 1$ if $\bm{\mathrm{x}} \in \mathcal{A}$, else $0$. In terms of convolution on 2-D images, $\mathcal{V}(i) = \{\bm{\mathrm{x}}^E :\bm{\mathrm{x}}^E - \bm{\mathrm{x}}_i^E \in \mathbb{Z}^2\cap ([-k_1, k_1] \times [-k_2, k_2])\}$. $k_1 > 0$ and $k_2 >0$ are the convolution views to restrict how far away pixels are included in the neighborhood along the width-axis and length-axis respectively. When $k_1, k_2 < +\infty$, the neighborhood set is limited, and thus the convolution is defined as **local**, conducted on each node's local space, with **local convolution kernel** $\chi(\cdot)$ . + +To extend the local convolution on generalized manifolds, we conclude that the local space of $\bm{\mathrm{x}}^E_i$ is $\mathcal{L}_{\bm{\mathrm{x}}_i^E}\mathbb{R}^2 = \{\bm{\mathrm{x}}^E :\bm{\mathrm{x}}^E - \bm{\mathrm{x}}_i^E \in[-k_1, k_1] \times [-k_2, k_2]\}$, so that the isometric map satisfies $\bm{\mathrm{v}}^E = \mathcal{M}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}^E) = \bm{\mathrm{x}}^E - \bm{\mathrm{x}}^E_i$. $\{-\bm{e}_{x}, -\bm{e}_y\}$ with $\bm{e}_{x}=(1,0)$ and $\bm{e}_{y}=(0,1)$ is the orthogonal basis in local coordinate system of the local space. In conclusion, $$\begin{align} + \bm{\mathrm{x}}^{i'}=\Pi_{\bm{\mathrm{x}}_i^E}(\bm{\mathrm{v}}^E) = -\bm{\mathrm{v}}^E = \bm{\mathrm{x}}_i^E - \bm{\mathrm{x}}^E . +\end{align}$$ In this way, we obtain the local convolution on 2-D Euclidean plane, which reads $$\begin{align} + (\Omega \star_{\mathcal{V}{(i)}} \bm{H})(\bm{\mathrm{x}}_i^E) &= \sum_{\bm{\mathrm{x}}^E} \chi(\bm{\mathrm{x}}^{i'}) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E). +\end{align}$$ In analogy to this, the local convolution on 2-D spherical the center node $\bm{\mathrm{x}}^S_i$ is defined similarly: $$\begin{align} + (\Omega \star_{\mathcal{V}{(i)}} \bm{H}) (\bm{\mathrm{x}}_i^E) + &=\sum_{\bm{\mathrm{x}}^S} \chi(\bm{\mathrm{x}}^{i'}) \bm{H}(\bm{\mathrm{x}}^S)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^S), +\end{align}$$ where $\mathcal{V}(i)$ is given by the graph structure, nodes in which can be mapped into $\bm{\mathrm{x}}_i^S$'s local space. And $\bm{\mathrm{x}}^{i'}$ is obtained by: $$\begin{align} + \bm{\mathrm{x}}^{i'}=\Pi_{\bm{\mathrm{x}}_i^S}(\bm{\mathrm{v}}^S) = \Pi_{\bm{\mathrm{x}}_i^S}(\mathcal{M}_{\bm{\mathrm{x}}_i^S}(\bm{\mathrm{x}}^S)) . +\end{align}$$ Following parts are organized to discuss how to elaborate + +- $\mathcal{M}_{\bm{\mathrm{x}}_i^S}(\cdot)$ and $\Pi_{\bm{\mathrm{x}}_i^S}(\cdot)$, isometry mapping neighbors into some local space of ${\bm{\mathrm{x}}_i^S}$ and choice of orthogonal basis in local coordinate system. + +- $\chi(\cdot)$, the formulation of convolution kernel to approximate and imitate the meteorological patterns. + +In the following parts, all the nodes are located on sphere, so the supper-script $S$ is omitted. We choose what we define as **cylindrical-tangent space** and **horizon maps** (Fig. 1(a).) to construct local spaces and to map neighbors into them. + +::: Definition +**Definition 2**. *For $\bm{\mathrm{x}}_i \in S^2$, the cylindrical-tangent space centered at $\bm{\mathrm{x}}_i$ reads $$\begin{align*} + \mathcal{C}_{\bm{\mathrm{x}}_i} S^2 = \{\bm{\mathrm{v}}\in \mathbb{R}^3:<\bm{\mathrm{v}}^-, \bm{\mathrm{x}}_i^-> = 0\}, +\end{align*}$$ where $\bm{\mathrm{x}}^- = (x_1, x_2)$, taking the first two coordinates of vectors in $\mathbb{R}^3$.* +::: + +::: Proposition +**Proposition 2**. *Similar to logarithmic map, the horizon map $\mathcal{H}_{\bm{\mathrm{x}}_i}(\cdot)$ is used to map the neighbor node $\bm{\mathrm{x}}_j\in \mathcal{V}(i)$ isometrically into $\mathcal{C}_{\bm{\mathrm{x}}_i} S^2$, which reads $$\begin{align*} + \mathcal{H}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j) = d_{S^2}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j)\frac{[P_{{\bm{\mathrm{x}}_i^-}}(\bm{\mathrm{x}}_j^- - \bm{\mathrm{x}}_i^-), x_{j,3} - x_{i,3}]}{||[P_{{\bm{\mathrm{x}}_i^-}}(\bm{\mathrm{x}}_j^- - \bm{\mathrm{x}}_i^-), x_{j,3} - x_{i,3}]||}, +\end{align*}$$ where $[\cdot,\cdot]$ is the concatenation of vectors/scalars.* +::: + +
+ +
(a) shows tangent space with logarithmic maps in the top and cylindrical-tangent space with horizon maps in the below. (b) shows the necessity of the unified standard for choice of the basis. xp from the east affects both xi and xj a lot, with the corresponding local patterns in two center nodes shown in the heatmaps. However, if the basis is not unified as given in the example, the smoothness of local convolution kernel will be compromised. (c) shows the motivation of reweighting the angle scale. The angle scale $\frac{\psi}{2\pi}$ is given to balance neighbors’ uneven contributions to the centers resulted from irregular distribution.
+
+ +The reason for choosing cylindrical-tangent space with horizon maps rather than tangent space with logarithmic maps is that the former one preserves the relative orientation on geographic graticules on the earth surface, which has explicitly geophysical meaning in meteorology. The logarithmic maps distort the relative position in orientation on graticules. For a node in the northern hemisphere, a neighbor locate in the east of it will locate in the north-east on its tangent plane after logarithmic map. In comparison, the defined cylindrical-tangent space preserves both relative orientation on graticules and spherical distance after mapping. Detailed proofs are provided Appendix B1 and empirical comparisons are given in Experiments 5.4. + +As discussed, the cylindrical-tangent space is Euclidean, so in $S^2$, the transform $\Pi_{\bm{\mathrm{x}}_i}(\cdot)$ can be determined by two orthogonal bases which are not unique. Since our method is mainly implemented in spherical meteorological signals, we choose $\{\bm{e}_{\phi},\bm{e}_z\}$ as the two orthogonal bases in every local coordinate system of the cylindrical-tangent plane, in order to permit every local space to share the consistent South and North poles and preserve the relative position. For $\bm{\mathrm{x}}_i = (x_{i,1}, x_{i,2}, x_{i,3})$ and $\bm{\mathrm{v}} \in \mathcal{C}_{\bm{\mathrm{x}}_i}S^2$, let $\phi_i = \arctan{(x_{i,2}/x_{i,1})}$, and $\bm{\mathrm{x}}^{i'} = \Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}) = (\theta^{i'}, z^{i'})$ which is obtained by $$\begin{align} + \phi^{i'} = <\bm{\mathrm{v}}, \bm{e}_{\phi_i}>;\quad + z^{i'} = <\bm{\mathrm{v}}, \bm{e}_{z_i}>, +\end{align}$$ which is the latitude and longitude on the sphere, and $$\begin{align} + \label{eq:orthobasis} +\bm{e}_{\phi_i} = (-\sin\phi_i, \cos\phi_i, 0); \quad +\bm{e}_{z_i} = (0, 0, 1). +\end{align}$$ The discussed maps and transforms cannot be applied for the South and North pole. We discuss it in Appendix B2. + +Now we introduce the conditional local convolution, which is the core module in our model. We aim to formulate a kernel which is + +- location-characterized: In the local regions of different center nodes, the meteorological patterns governed by convolution kernel differ. + +- smooth: Patterns are broadly similar when the center nodes are close in spatial distance. + +- common: The kernel is shared by different local spaces where the neighbors' spatial distribution is distinct. + +Contrary to Example 1 in DCRNN whose convolution kernel is predefined, we aim to propose the convolution kernel which can adaptively learn and imitate the location-characterized patterns of each local region centered at node $i$. A trivial way is to use a multi-layer neural network whose input is $\bm{\mathrm{x}}^{i'}$ to approximate the convolution kernel $\chi({\bm{\mathrm{x}}^{i'}})$. However, ${\bm{\mathrm{x}}^{i'}}$ as the input only represents the relative position and disables the kernel to capture the location-characterized patterns. For example, given two different center nodes whose neighbors' relative positions are totally the same, the convolution kernel in different locations will also coincide exactly, contrary to location-characterized patterns. Therefore, we propose to use conditional kernel, which reads $\chi({\bm{\mathrm{x}}^{i'}} ; \bm{\mathrm{x}}_{i})$, meaning that the convolution kernel in a certain local region is determined by the center node $\bm{\mathrm{x}}_{i}$. An multi-layer feedforward network is used to approximate this term, as $$\begin{align} + \chi({\bm{\mathrm{x}}^{i'}} ; \bm{\mathrm{x}}_{i}) = \mathrm{MLP}([\bm{\mathrm{x}}^{i'} , \bm{\mathrm{x}}_{i}]). \label{eq:mlpkernel} +\end{align}$$ + +We assume that the localized patterns of meteorological message flows have the property of **smoothness** -- two close center nodes' patterns of aggregation of messages from neighbors should be similar. In the light of convolution kernel, we define the smoothness of kernel function as follows: + +::: Definition +**Definition 3**. *The conditional kernel $\chi(\cdot|\cdot)$ is smooth, if it satisfies that for any $\epsilon > 0$, there exist $\delta>0$, such that for any two points $\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j \in {S^2}$ with $d_{S^2}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j) \leq \delta$, $$\begin{align*} +\sup_{\bm{\mathrm{v}}\in \mathcal{C}_{\bm{\mathrm{x}}_i} {S^2},\bm{\mathrm{u}}\in \mathcal{C}_{\bm{\mathrm{x}}_j} {S^2}\atop \Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}) = \Pi_{\bm{\mathrm{x}}_j}(\bm{\mathrm{u}})} |\chi(\Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}});\bm{\mathrm{x}}_j) - \chi(\Pi_{\bm{\mathrm{x}}_j}(\bm{\mathrm{u}});\bm{\mathrm{x}}_i)|\leq \epsilon. +\end{align*}$$* +::: + +The definition of smoothness of location-characterized kernel is motivated by the fact that if the distance between two center nodes $d_{S^2}(\bm{\mathrm{x}}_{i}, \bm{\mathrm{x}}_{j})$ is very small, the meteorological patterns in two local region should be of little difference, and thus kernel function $\chi(\cdot;\bm{\mathrm{x}}_{i})$ and $\chi(\cdot;\bm{\mathrm{x}}_{j})$ should be almost exactly the same. + +The unified standard for choice of orthogonal basis on the cylindrical-tangent plane avoids problems caused by path-dependent parallel transport [@cohen2019gauge], and contributes to the smoothness of conditional kernel. The property is likely to be compromised without unified standard for orthogonal basis, as the following example illustrates. + +::: Example +**Example 4**. *(shown in Fig. [\[fig:exampleunify\]](#fig:exampleunify){reference-type="ref" reference="fig:exampleunify"}.) For one node $\bm{\mathrm{x}}_{i}$, the orthogonal basis is $\{\mathbf{e}_{\phi_i}, \mathbf{e}_{z_i}\}$ as previously defined in Eq. [\[eq:orthobasis\]](#eq:orthobasis){reference-type="ref" reference="eq:orthobasis"}, and for another node $\bm{\mathrm{x}}_{j}$ which is close to it, it is $\{-\mathbf{e}_{\phi_i}, \mathbf{e}_{z_i}\}$. There exists a node $\bm{\mathrm{x}}_{p}$ which is in their east on the sphere as the neighbors of both, and has great meteorological impacts on both of them. In one's local coordinate system, the first coordinate is positive while it is negative in another. Then if the kernel is smooth, the neighbor $\bm{\mathrm{x}}_{p}$ from the east will never be given large value in both local regions centered at node $i$ and $j$, violating the true patterns in meteorology, or it is likely to violate the smoothness of the kernel.* +::: + +As such, by using $\mathrm{MLP}(\cdot)$ as the approximator with smooth activate function like $\tanh$ and unifying the standard for choice of orthogonal basis, the smoothness property of conditional kernel can be ensured. However, the irregular spatial distribution of discrete nodes conflicts with the continuous kernel function shared by different center nodes, which will be discussed in the next part. + +Because the kernel function is continuous and shared by different center nodes, when the spatial distribution of each node's neighbors is similar or even identical, e.g. nodes are distributed on regular spatial grids in local spaces, the proposed conditional kernel takes both distance and orientation into consideration. However, the nodes are discrete and irregularly distributed on the sphere. Since the kernel is shared by all center nodes, the distinct spatial distribution of neighbors of different center nodes is likely to disrupt the smoothness of local patterns. An explicit example is given to illustrate the problems brought about by it. + +::: Example +**Example 5**. *(shown in Fig. [\[fig:examplereweight\]](#fig:examplereweight){reference-type="ref" reference="fig:examplereweight"}.) The two center nodes are close in distance, but the spatial distribution of their neighbors is different. The number of the right center's neighbors located in the south-west is two, while it is one for the left center. If the kernel is smooth, the message from the south-west flowing into the right center will be about twice than it from the south-west flowing into the left.* +::: + +To reweight the convolution kernel for each $\bm{\mathrm{x}}_{j} \in \mathcal{V}(i)$, we consider both their angle and distance scales. We first turn its representation in Cartesian coordinate system $\bm{\mathrm{x}}_j^{i'} = (\phi^{i'}_j, z^{i'}_j)$ in cylindrical-tangent space of $\bm{\mathrm{x}}_i$ into polar coordinate $(\varphi^{i'}_j, \rho^{i'}_j)$, where $\varphi^{i'}_j = \arctan (z^{i'}_j/\phi^{i'}_j)$ and $\rho^{i'}_j = \sqrt{(z^{i'}_j)^2 + (\phi^{i'}_j)^2}$. Note that $\rho^{i'}_j$ equals to the geodesics induced distance between the two nodes on sphere. In terms of angle, we calculate the **angle bisector** of every pair of adjacent nodes in the neighborhood according to $\varphi^{i'}_j$. We denote the angle between two adjacent angular bisectors of $\bm{\mathrm{x}}_j^{i'}$ by $\psi^{i'}_j$ (as shown in lower-right subfigure in Fig. [\[fig:examplereweight\]](#fig:examplereweight){reference-type="ref" reference="fig:examplereweight"} ), and thus the angle scale is written as $\psi^{i'}_j/2\pi$. The distance scale is obtained similarly as DCRNN in Example 1, which reads $\exp(- (\rho^{i'}_j)^2/\tau)$, where $\tau$ is a learnable parameter. + +To sum up, combining the two scaling terms with Eq. [\[eq:mlpkernel\]](#eq:mlpkernel){reference-type="ref" reference="eq:mlpkernel"}, the final formulation of the smooth conditional local kernel in the case of irregular spatial distribution reads $$\begin{align} + \chi({\bm{\mathrm{x}}^{i'}_j} ; \bm{\mathrm{x}}_{i}) = \frac{\psi^{i'}_j}{2\pi}\exp(- \frac{(\rho^{i'}_j)^2}{\tau})\mathrm{MLP}([\bm{\mathrm{x}}^{i'}_j , \bm{\mathrm{x}}_{i}]). \label{eq:clckernel} +\end{align}$$ + +
+


+

+
Different geographic sample spaces and local patterns in meteorology and traffic.
+
+ +The proposed convolution is inapplicable to traffic forecasting. One reason is that the smoothness is not a reasonable property of local traffic flows' patterns, i.e. great difference may exist between traffic patterns of two close regions. An important transportation hub exit may exist in the middle of them, so the patterns are likely to differ a lot. Besides, because our convolution kernel is continuous in spatial domain, the continuity in orientation of the local convolution kernel is of no physical meaning in traffic irregular networks. In essence, the irregular structure of the road network restricts the flows of traffic to road direction, stopping vehicles from crossing the road boundary, so that the geographic sample space is restricted to the road networks, and traffic can only flow along roads. In comparison, the flows in meteorology like heat and wind can diffuse freely on the earth, without boundary, and the geographic sample space is the whole earth surface, enabling the local patterns to satisfy the continuity and smoothness. + +
+ +
Overall workflows and architecture of CLCRN.
+
+ +The temporal dynamics is modeled as DCRNN does, which replaces fully-connected layers in cells of recurrent neural network with graph convolution layers. Using the kernel proposed in Eq. [\[eq:clckernel\]](#eq:clckernel){reference-type="ref" reference="eq:clckernel"}, we obtain the GRU cell constituting the conditional local convolution recurrent network (CLCRN), whose overall architecture is shown in Fig. [1](#fig:overallarchi){reference-type="ref" reference="fig:overallarchi"}. + +The overall neural network architecture for multi-step forecasting is implemented based on Sequence to Sequence framework [@Sutskever2014seq2seq], with the encoder fed with previously observed time series and decoder generating the predictions. By setting a target function of predictions and ground truth observations such as minimal mean square error, we can use backpropagation through time to update the parameters in the training step. More details are given in Appendix C. diff --git a/2104.06313/main_diagram/main_diagram.drawio b/2104.06313/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..b4917aa72db40f0397404493ae3dbbed70daedc0 --- /dev/null +++ b/2104.06313/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2104.06313/main_diagram/main_diagram.pdf b/2104.06313/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5f4b86e928fc7f02f34f4b82ed5debc5905b1377 Binary files /dev/null and b/2104.06313/main_diagram/main_diagram.pdf differ diff --git a/2104.06313/paper_text/intro_method.md b/2104.06313/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..5aa47aa2b384354b4c810b71e330304f18eab6e1 --- /dev/null +++ b/2104.06313/paper_text/intro_method.md @@ -0,0 +1,149 @@ +# Introduction + +In many real-world NLP applications, the collected data follow a skewed distribution [\(Deng et al.,](#page-9-0) [2009;](#page-9-0) [Fernandez et al.](#page-9-1) ´ , [2013;](#page-9-1) [Yan et al.,](#page-10-0) [2017\)](#page-10-0), i.e., data from a few classes appear much more frequently than those of other classes. For example, tweets related to incidents such as shooting or fire are usually rarer compared to those about sports or entertainments. These data instances often represent objects of interest as their rareness may carry important and useful knowledge [\(He](#page-9-2) [and Garcia,](#page-9-2) [2009;](#page-9-2) [Sun et al.,](#page-10-1) [2007;](#page-10-1) [Chen and Shyu,](#page-9-3) [2011\)](#page-9-3). However, most learning algorithms tend to inefficiently utilize them due to their disadvantage in the population [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4). Hence, learning discriminative models with imbalanced class distribution is an important and challenging task to the machine learning community. + +Solutions proposed in previous literature can be generally divided into three categories [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4): (1) *Data-level methods* that employ undersampling or over-sampling technique to balance the class distributions [\(Barua et al.,](#page-9-5) [2014;](#page-9-5) [Smith et al.,](#page-9-6) [2014;](#page-9-6) [Sobhani et al.,](#page-9-7) [2014;](#page-9-7) [Zheng et al.,](#page-10-2) [2015\)](#page-10-2). (2) *Algorithm-level methods* that modify existing learners to alleviate their bias towards the majority classes. The most popular branch is the *costsensitive* algorithms, which assign a higher cost on misclassifying the minority class instances. [\(D´ıaz-](#page-9-8)[Vico et al.,](#page-9-8) [2018\)](#page-9-8). (3) *Ensemble-based methods* that combine advantages of data-level and algorithmlevel methods by merging data-level solutions with classifier ensembles, resulting in robust and efficient learners [\(Galar et al.,](#page-9-9) [2012;](#page-9-9) [Wang et al.,](#page-10-3) [2015\)](#page-10-3). + +Despite the success of these approaches on many applications, some of their drawbacks have been observed. Resampling-based methods need to either remove lots of samples from the majority class or introduce a large amount of synthetic samples to the minority class, which may respectively lose important information or significantly increase the adverse correlation among samples [\(Wu et al.,](#page-10-4) [2017\)](#page-10-4). It is difficult to set the actual cost value in costsensitive approaches and they are often not given by expert before hand [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4). Also, how to guarantee and utilize the diversity of classification ensembles is still an open problem in ensemble-based methods [\(Wu et al.,](#page-10-4) [2017;](#page-10-4) [Huo](#page-9-10) [et al.,](#page-9-10) [2016\)](#page-9-10). + +In this paper, we propose a novel *set convolution* (SetConv) operation and a new training strategy named as *episodic training* to assist learning from imbalanced class distributions. The proposed solution naturally addresses the drawbacks of existing methods. Specifically, SetConv explicitly learns the weights of convolution kernels based on the intra-class and inter-class correlations, and uses the learned kernels to extract discriminative features from data of each class. It then compresses these features into a single class representative. These representatives are later applied for classification. Thus, SetConv helps the model to *ignore samplespecific noisy information*, and *focuses on the la-* + +![](_page_1_Picture_0.jpeg) + +Figure 1: Overview of the proposed approach. (a) The training procedure of SetConv. At each iteration, SetConv is fed with an episode to evaluate the classification loss for model update. Each episode consists of a support set and a query set. The support set is formed by a group of samples where the imbalance ratio is preserved. The query set contains only one sample from each class. (b) The post training step of SetConv, which is performed only once after the main training procedure. In this step, we extract a representative for each class from the training data and will later use them for inference. Here we only perform inference using the trained model and do not update it. (c) The inference procedure of SetConv. Each query data is compared with every class representative to determine its label. + +*tent concept not only common to different samples of the same class but also discriminative to other classes.* On the other hand, in episodic training, we assign equal weights to different classes and do not perform resampling on data. Moreover, at each iteration during training, the model is fed with an episode formed by a set of samples where the class imbalance ratio is preserved. It encourages the model learning to *extract discriminative features even when class distribution is highly unbalanced*. + +Building models with SetConv and episodic training has several additional benefits: + +- (1) *Data-Sensitive Convolution.* By utilizing SetConv, each input sample is associated with a set of weights that are estimated based on its relation to the minority class. This data-sensitive convolution helps the model to customize the feature extraction process for each input sample, which potentially improves the model performance. +- (2) *Automatic Class Balancing.* At each iteration, no matter how many data of a class is fed into the model, SetConv always extracts the most discriminative information from them and compress + +it into a single distributed representation. Thus, the subsequent classifier, which takes these class representatives as input, always perceives a balanced class distribution. + +(3) *No dependence on unknown prior knowledge.* The only prior knowledge needed in episodic training is the class imbalance ratio, which can be easily obtained from data in real-world applications. + +# Method + +Our goal is to develop a classification model that works well when the class distribution is highly unbalanced. For simplicity, we first consider a binary classification problem and later extend it to the multi-class scenario. As shown in Fig. [1a,](#page-1-0) our model is composed of a SetConv layer and a classification layer. At each iteration during training, the model is fed with an *episode* sampled from the training data, which is composed of a support set and a query set. The support set preserves the imbalance ratio of training data, and the query set contains only one sample from each class. Once the SetConv layer receives an episode, it extracts features for every sample in the episode and produces a representative for each class in the support set. Then, each sample in the query set is compared with these class representatives in classification layer to determine its label and evaluate the classification loss for model update. We refer this training procedure as *episodic training*. + +We choose episodic training due to following reasons: (1) It encourages the SetConv layer learning to extract discriminative features even when the class distribution of the input data is highly unbalanced. (2) Since the episodes are randomly sampled from data with significantly different configuration of support and query sets (i.e., data forming these sets vary from iteration to iteration), it requires the SetConv layer to capture the underlying class concepts that are common among different episodes. + +After training, a post training step is performed only once to extract a representative for each class from the training data, which will later be used for inference (Fig. [1b\)](#page-1-1). It is conducted by randomly sampling a large subset of training data (referred as Spost) and feeding them to the SetConv layer. *Note that we only perform inference using the trained model and do not update it in this step.* We can conduct this operation because the SetConv layer has learned to capture the class concepts, which are insensitive to the episode configuration during training. We demonstrate it in experiments and the result is shown in Section [4.6.](#page-8-0) + +The inference procedure of the proposed ap- + +![](_page_3_Figure_0.jpeg) + +Figure 2: Relations between the input samples and a pre-selected minority class anchor are used by SetConv to estimate both intra-class correlations and inter-class correlations. + +proach is straightforward (Fig. 1c). For each query sample, we extract its feature via the SetConv layer and then compare it with those class representatives obtained in post training step. The class that is most similar to the query is assigned as the predicted label. + +In many real-world applications, the minority class instances often carry important and useful knowledge that need intensive attention by the machine learning models (He and Garcia, 2009; Sun et al., 2007; Chen and Shyu, 2011). + +Based on this prior knowledge, we choose to design the SetConv layer in a way such that the feature extraction process focuses on the minority class. We achieve it by estimating the weights of the SetConv layer based on the relation between the input samples and a pre-selected minority class anchor. This anchor can be freely determined by the user. In this paper, we adopt a simple option, i.e., average-pooling of the minority class samples. Specifically, for each input variable, we compute its mean value across all the minority class samples in the training data. It is executable because the minority-class samples are usually limited in realworld applications1. As shown in Figure 2, this weight estimation method assists the SetConv layer in capturing not only the intra-class correlation of the minority class, but also the inter-class correlation between the majority and minority classes. + +Suppose $\mathcal{E}_t = \{\mathcal{S}_t, \mathcal{Q}_t\}$ is the episode sent to the SetConv layer at iteration t, where $\mathcal{S}_t = (X_{maj} \in \mathcal{R}^{N_1 \times d}, X_{min} \in \mathcal{R}^{N_2 \times d})$ is the support set and $\mathcal{Q}_t = (q_{maj} \in \mathcal{R}^{1 \times d}, q_{min} \in \mathcal{R}^{1 \times d})$ is the query set. In general, $X_{maj}, X_{min}, q_{maj}$ and $q_{min}$ can be considered as a sample set of size $N_1, N_2, 1$ and 1 respectively. For simplicity, we abstract this sample set into $X \in \mathcal{R}^{N \times d}, N \in \{N_1, N_2, 1\}$ . + +Remind that the standard discrete convolution is: + +$$h[n] = (f \star g)[n] = \sum_{m=-M}^{m=M} f[m]g[n-m]$$ + (1) + +Here, f and g denote the feature map and kernel weights respectively. + +Similarly, in our case, we define the set convolution (SetConv) operation as: + + +$$h[Y] = \frac{1}{N} \sum_{i=1}^{N} X_i \cdot g(Y - X_i)$$ +$$= \frac{1}{N} \left( X \circ g(Y - X) \right)$$ +(2) + +where $Y \in \mathcal{R}^{1 \times d}$ , $g(Y - X) \in \mathcal{R}^{N \times d \times d_o}$ and $h[Y] \in \mathcal{R}^{1 \times d_o}$ denote the minority class anchor, kernel weights and the output embedding respectively. Here, $\circ$ is the tensor dot product operator, i.e., for every $i \in \{1, 2, \dots, d_o\}$ , we compute the dot product of X and g(Y - X)[:,:,i]. + +Unfortunately, directly learning g(Y-X) is memory intensive and computationally expensive, especially for large-scale high-dimensional data. To overcome this issue, we introduce an efficient method to approximate these kernel weights. Instead of taking X as a set of d-dimensional samples, we stack these samples and consider it as a giant dummy sample $X' = Concat(X) \in \mathcal{R}^{1 \times Nd}$ . Then, Eq. 2 is rewritten as + +$$h[Y] = \frac{1}{N} \Big( X' \cdot g'(Y - X) \Big) \tag{3}$$ + +where $g'(Y-X) \in \mathcal{R}^{Nd \times d_o}$ is the transformed kernel weights. To efficiently compute g'(Y-X), we propose to approximate it as the *Khatri-Rao* product2 (Rabanser et al., 2017) of two individual components, i.e., + +$$g'(Y - X) = g_1(Y - X) \circledast g_2(W)$$ + +$$= MLP(Y - X; \theta) \circledast SoftMax(W, 0)$$ +(4) + +&lt;sup>1Otherwise, we may sample a subset from the minority class samples to compute the anchor. + +&lt;sup>2https://en.wikipedia.org/wiki/Kronecker\_product + +![](_page_4_Figure_0.jpeg) + +Figure 3: The computation graph of the SetConv layer. Here Y is a minority class anchor. $W \in \mathcal{R}^{d \times d_o}$ is a weight matrix to learn that records the correlation between the input and output variables. Specifically, the $i_{th}$ column of $g_2(W)$ gives the weight distribution over input features for the $i_{th}$ output feature. It is indeed a feature-level attention matrix. In addition, we estimate another data-sensitive weight matrix $g_1(Y-X)$ from the input data. The final convolution weight tensor is simply the Khatri-Rao product of $g_1(Y - X)$ and $g_2(W)$ . + +where $W \in \mathcal{R}^{d \times d_o}$ is a weight matrix that represents the correlation between input and output variables. $g_2(W)$ takes softmax over the first dimension of W, and is indeed a feature-level attention matrix. The $i_{th}$ column of $g_2(W)$ provides the weight distribution over input features for the $i_{th}$ output feature. On the other hand, $g_1(Y-X)$ is a data-sensitive weight matrix estimated from input data via a MLP by considering their relation to the minority class anchor. Similar to data-level attention, $g_1(Y - X)$ helps the model customize the feature extraction process for input samples, which potentially improves the model performance. Figure 3 shows the detailed computation graph of the SetConv layer. + +**Discussion:** An important property of the Set-Conv layer is *permutation-invariant*, i.e., it is insensitive to the order of input samples. As long as the input samples are same, no matter in which order they are sent to the model, the SetConv layer always produces the same feature representation. Mathematically, let $\pi$ denote an arbitrary permutation matrix, we have $SetConv(\pi X) =$ SetConv(X). The detailed proof of this property is provided in the supplementary material. + +Suppose the feature representation obtained from the SetConv layer for $X_{maj}$ , $X_{min}$ , $q_{maj}$ and $q_{min}$ in the episode are denoted by $v_{maj}^s$ , $v_{min}^s$ , $v_{maj}^q$ and $v_{min}^q$ respectively. The probability of predicting $v_{maj}^q$ or $v_{min}^q$ as the majority class is given by + +$$P(c=0|x) = \frac{\exp(x \odot v_{maj}^s)}{\exp(x \odot v_{maj}^s) + \exp(x \odot v_{min}^s)}$$ +(5) + +where o represents the dot product operation and $x \in \{v_{maj}^q, v_{min}^q\}.$ Similarly, the probability of predicting $v_{maj}^q$ or + + $v_{min}^q$ as the minority class is + +$$P(c=1|x) = \frac{\exp(x \odot v_{min}^s)}{\exp(x \odot v_{maj}^s) + \exp(x \odot v_{min}^s)}$$ +(6) + +where $x \in \{v_{maj}^q, v_{min}^q\}$ . + +We adopt the well-known cross-entropy loss for error estimation and use the Adam optimizer to update model. + +Extending SetConv for multi-class imbalance learning is straightforward. We translate the multi-class classification problem into multiple binary classification problems, i.e., we create a one-vs-all classifier for each of the N classes. Specifically, for a class c, we treat those instances with label y = cas positive and those with $y \neq c$ as negative. The anchor is hence computed based on the smaller one of the positive and negative classes. The prediction probability P(y = c|x) for a given instance x is computed in a similar way as Eq. 5, + +$$P(y=c|x) = \frac{\exp(x \odot v_{y=c}^s)}{\exp(x \odot v_{y\neq c}^s) + \exp(x \odot v_{y=c}^s)}$$ +(7) + +Therefore, the predicted label of the instance x is $\operatorname{argmax}_{c} P(y = c | x).$ + +We evaluate SetConv on two typical tasks, including incident detection on social media and sentiment classification. + +Table 1: Class distribution in the IRT dataset. + +| | Two Classes | | Four Classes | | | | +|----------------|-------------|------|--------------|------|----------|------| +| | Yes | No | Crash | Fire | Shooting | No | +| Boston (USA) | 604 | 2216 | 347 | 188 | 28 | 2257 | +| Sydney (AUS) | 852 | 1991 | 587 | 189 | 39 | 2208 | +| Brisbane (AUS) | 689 | 1898 | 497 | 164 | 12 | 1915 | +| Chicago (USA) | 214 | 1270 | 129 | 81 | 4 | 1270 | +| Dublin (IRE) | 199 | 2616 | 131 | 33 | 21 | 2630 | +| London (UK) | 552 | 2444 | 283 | 95 | 29 | 2475 | +| Memphis (USA) | 361 | 721 | 23 | 30 | 27 | 721 | +| NYC (USA) | 413 | 1446 | 129 | 239 | 45 | 1446 | +| SF (USA) | 304 | 1176 | 161 | 82 | 61 | 1176 | +| Seattle (USA) | 800 | 1404 | 204 | 153 | 139 | 390 | + +Table 2: Class distribution in Amazon Review and SemiEval Datasets. + +| Dataset | Negative | Positive | IR | +|--------------------|----------|----------|------| +| Amazon-Books | 72039 | 7389 | 9.75 | +| Amazon-Electronics | 13560 | 1908 | 7.11 | +| Amazon-Movies | 12896 | 2066 | 6.24 | +| SemiEval | 39123 | 7273 | 5.38 | + +We conduct experiments on a real-world benchmark *Incident-Related Tweet*3 (Schulz et al., 2017) (**IRT**) dataset. It contains 22, 170 tweets collected from 10 cities, and allows us to evaluate our approach against geographical variations. The IRT dataset supports two different problem settings: binary classification and multi-class classification. In binary classification, each tweet is either "incident-related" or "not incident-related". In multi-class classification, each tweet belongs to one of the four categories including "crash", "fire", "shooting" and a neutral class "not incident related". The details of this dataset are shown in Table 1. + +We conduct experiments on two large-scale benchmark datasets, including *Amazon Review*4 (He and McAuley, 2016) and *SemiEval*5 (Rosenthal et al., 2017), which have been widely used for sentiment classification. Similar to MSDA (Li et al., 2019) and SCL-MI (Blitzer et al., 2007), we treat the amazon reviews with rating > 3 as positive examples, those with rating < 3 as negative examples, and discard the rest because their polarities are ambiguous. In addition, due to the tremendous size of Amazon Review dataset, we choose its 3 largest categories, i.e., "Books", "Electronics", and "Movies and TV", + +and uniformly sample from these categories to form a subset that contains 109, 858 reviews. This subset is sufficiently large to evaluate the effectiveness of our method. More importantly, the imbalance ratio of each category in this subset is exactly same as that in the original dataset. Details of Amazon Review and SemiEval datasets are listed in Table 2. + +We compare our algorithm with several state-ofthe-art imbalance learning methods. + +- **IHT** (Smith et al., 2014) (*under-sampling*) is a model that performs undersampling based on instance hardness. +- WEOB2 (Wang et al., 2015) (ensemble) is an undersampling based ensemble model that effectively adjusts the learning bias from the majority class to the minority class via adaptive weight adjustment. It only supports binary classification. +- **KMeans-SMOTE** (Last et al., 2017) (*over-sampling*) is an oversampling technique that avoids the generation of noisy samples and effectively overcomes the imbalance between classes. +- IML (Wang et al., 2018) (*metric learning*) is a method that utilizes metric learning to explore the correlations among imbalance data and constructs an effective data space for classification. +- **CS-DMLP** (Díaz-Vico et al., 2018) (*cost-sensitive*) is a deep MLP model that utilizes cost-sensitive learning to regularize the posterior probability distribution predicted for a given sample. diff --git a/2104.08790/main_diagram/main_diagram.drawio b/2104.08790/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..4873eab8f2cf3607f6386e32bb5554ceeebced25 --- /dev/null +++ b/2104.08790/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2104.08790/main_diagram/main_diagram.pdf b/2104.08790/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7bc1856f3770706f3fb606129f80d5fd0347f663 Binary files /dev/null and b/2104.08790/main_diagram/main_diagram.pdf differ diff --git a/2104.08790/paper_text/intro_method.md b/2104.08790/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..c9543af78e4f4aa6bd83a85e9c8e00d39710da22 --- /dev/null +++ b/2104.08790/paper_text/intro_method.md @@ -0,0 +1,141 @@ +# Introduction + +
+ +
In a binary classification setup, the reaction of the reader is unclear. Here, we use Misinfo Reaction Frames to understand how the reader perceives and reacts to the headline. Our pragmatic frames explain how a health or climate news article is interpreted as reliable or misinformation by readers by incorporating not only linguistic knowledge (e.g. emotions invoked by certain content words), but also knowledge of common social behaviors and domain-specific reasoning. We also include fact-checked labels (gold label).
+
+ +:::: table* +::: tabular +F10emF11 emF11.5emcc **News Headline** & **Writer's Intent** & **Reader Reaction** & **Spread** & **Real News?**\ +& & & & (GPT-2 / T5 / Gold)\ +How COVID is Affecting U.S. Food Supply Chain & **Human**: "the pandemic is interrupting the flow of groceries to consumers\"\ +**GPT-2**: "food supplies are being affected by covid\"\ +**T5**: "food supply chain is affected by covid\" & **Human**: "want to know how their groceries will get to them\"\ +**GPT-2**: "want to learn more\"\ +**T5**:"want to find out more information\" & 4.0 & / /\ +\ +Thai police arrested a cat for disobeying the curfew order. & **Human**: "governments are ludicrous and obtuse.\"\ +**GPT-2**: "animals can be dangerous\"\ +**T5**: "lockdowns are enforced in thailand\" & + +**Human**: "feel disbelief\"\ +**GPT-2**: "feel worried\"\ +**T5**: "feel shocked\" & 1.0 & / /\ +\ +Perspective \| I'm a black climate expert. Racism derails our efforts to save the planet. & **Human**: "since climate change will likely affect poorer nations, rich societies are not motivated to help\"\ +**GPT-2**: "racism is bad\"\ +**T5**: "racism is a problem in society\" & **Human:** "want to improve their own behavior towards others\"\ +**GPT-2:** "want to learn more\"\ +**T5**: "want to take action\" & 3.0 & / /\ +::: +:::: + +> *Many objects, persons, and experiences in the world are framed in terms of their potential role in supporting, harming, or enhancing people's lives or interests. We can know that this is so if we know how to interpret expressions in which such things are evaluated\...*\ +> - Charles J. Fillmore (1976) + +Effectively predicting how a headline may influence a reader requires knowledge of how readers perceive the intent behind real and fake news. While most prior NLP research on misinformation has focused on fact-checking, preventing spread of misinformation goes beyond determining veracity [@schuster2020limitations; @social_motives]. + +For example, in Figure [1](#fig:mars){reference-type="ref" reference="fig:mars"}, mistrust in the government may lead readers to share pandemic conspiracy headlines like *"Epidemics and cases of disease in the 21st century are "staged\"\"* even if they suspect it is misinformation. The widespread circulation of misinformation can have serious negative repercussions on readers --- it can reinforce sociopolitical divisions like anti-Asian hate [@Vidgen2020DetectingEA; @voterfraud], worsen public health risks [@10.1145/3274327], and undermine efforts to educate the public about global crises [@climate]. + +We introduce **Misinfo Reaction Frames** (MRF), a pragmatic formalism to reason about the effect of news headlines on readers. Inspired by Frame semantics [@Fillmore1976FRAMESA], our frames distill the pragmatic implications of a news headline in a structured manner. We capture free-text explanations of readers reactions and perceived author intent, as well as categorical estimates of veracity and likelihood of spread (Table [\[table:dims\]](#table:dims){reference-type="ref" reference="table:dims"}). We use our new formalism to collect the MRF corpus, a dataset of 202.3k news headline/annotated dimension pairs (69.8k unique implications for 25.1k news headlines) from Covid-19, climate and cancer news. + +We train reaction inference models to predict MRF dimensions from headlines. As shown by Table [\[table:examples_data\]](#table:examples_data){reference-type="ref" reference="table:examples_data"}, reaction inference models can correctly label the veracity of headlines (85% F1) and infer commonsense knowledge like *"a cat being arrested for disobeying curfew $\implies$ lockdowns are enforced.\"* However, models struggle with more nuanced implications *"a cat arrested for disobeying curfew $\implies$ government incompetence.\"* We test generalization of reaction frame inference on a new cancer domain and achieve 86% F1 by finetuning our MRF model on 574 annotated examples. + +:::: table* +::: tabular +llF18emF12em **Dimension** & **Type** & **Description** & **Example**\ +Writer Intent & free-text & A writer intent implication captures **the readers' interpretation of what the writer is implying.** & "some masks are better than others.\"\ +&\ +Reader Perception & free-text & A reader perception implication describes how readers would *feel* in response to a headline. These inferences include **emotional reactions** and **observations**. & "feeling angry.\", "feeling that the event described in the headline would trouble most people.\"\ +&\ +Reader Action & free-text & A reader action implication captures what readers would *do* in response to a headline. These describe **actions**. & "buy a mask.\"\ +&\ +Likelihood of Spread & ordinal & To take into account variability in impact of misinformation due to low or high appeal to readers, we use a 1-5 Likert [@Likert1932ATF] scale to measure the **likelihood of an article being shared or read**. Categories are {*Very Likely*, *Likely*, *Neutral*, *Unlikely*, *Very Unlikely*}. & 4/5\ +&\ +Perceived Label & binary & We elicit the perceived label (real/misinfo) of a headline, i.e. **whether it appears to be misinformation or real news to readers**. & real\ +&\ +Gold Label & binary & We include the **original ground-truth headline label** (real/misinfo) that was verified by fact-checkers. & misinfo\ +::: +:::: + +To showcase the usefulness of the MRF framework in user-facing interventions, we investigate the effect of MRF explanations on reader trust in headlines. Notably, in a user study our results show that machine-generated MRF inferences affect readers' trust in headlines and for the best model there is a statistically significant correlation (Pearson's $r$=0.24, $p$=0.018) with labels of trustworthiness (§[5.3](#sec:gen){reference-type="ref" reference="sec:gen"}). + +Our framework and corpus highlight the need for reasoning about the pragmatic *implications* of news headlines with respect to reader reactions to help combat the spread of misinformation. We publicly release the MRF corpus and trained models to enable further work ().[^1] We explore promising future directions (and limitations) in (§[6](#sec:future_work){reference-type="ref" reference="sec:future_work"}). + +In contrast to prior work on misinformation detection [@ott-etal-2011-finding; @rubin-etal-2016-fake; @rashkin-etal-2017-truth; @Wang2017LiarLP; @Hou2019TowardsAD; @volkova-etal-2017-separating; @10.1145/3274351] which mostly focuses on linguistic or social media-derived features, we focus on the potential impact of a news headline by modeling readers' reactions. This approach is to better understand how misinformation can be countered, as it has been shown that interventions from AI agents are better at influencing readers than strangers [@Kulkarni2013AllTN]. + +In order to model impact, we build upon prior work that aims to describe the rich interactions involved in human communication, including semantic frames [@Fillmore1976FRAMESA], the encoder-decoder theory of media [@Hall1973EncodingAD][^2], Grice's conversational maxims [@grice1975logic] and the rational speech act model [@GOODMAN2016818][^3]. By describing these interactions with free-text implications invoked by a news headline, we also follow from prior work on pragmatic frames of connotation and social biases [@speer-havasi-2012-representing; @rashkin-etal-2018-event2mind; @Sap2019ATOMICAA; @socialbf; @socialc]. + +While approaches like rational speech acts model both a pragmatic speaker and listener, we take a **reader-centric** approach to interpreting "intent\" of a news headline given that the writer's intent is challenging to recover in the dynamic environment of social media news sharing [@10.1145/3359229]. By bridging communication theory, data annotation schema and predictive modeling, we define a concrete framework for understanding the impact of a news headline on a reader. + +Table [\[table:examples_data\]](#table:examples_data){reference-type="ref" reference="table:examples_data"} shows real and misinformation news examples from our dataset with headlines obtained from sources described in §[3.1](#sec:data){reference-type="ref" reference="sec:data"}. We pair these headline examples with generated reaction frame annotations from the MRF corpus. Each reaction frame contains the dimensions in Table [\[table:dims\]](#table:dims){reference-type="ref" reference="table:dims"}. + +We elicit annotations based on a *news headline*, which summarizes the main message of an article. We explain this further in §[3.1](#sec:data){reference-type="ref" reference="sec:data"}. An example headline is "*Covid-19 may strike more cats than believed*.\" To simplify the task for annotators and ground implications in real-world concerns, we define these implications as relating to one of 7 common themes (e.g. technology or government entities) appearing in Covid and climate news.[^4] We list all the themes in Table [1](#table:themes){reference-type="ref" reference="table:themes"}, with some themes being shared between topics. + +To construct a corpus for studying reader reactions to news headlines, we obtain 69,885 news implications (See §[3.1](#sec:data){reference-type="ref" reference="sec:data"}) by eliciting annotations for 25,164 news headlines (11,757 Covid related articles, 12,733 climate headlines and 674 cancer headlines). There are two stages for collecting the corpus - (1) news data collection and (2) crowd-sourced annotation. + +A number of definitions have been proposed for labeling news articles based on reliability. To scope our task, we focus on false news that may be unintentionally spread (misinformation). This differs from disinformation, which assumes a malicious intent or desire to manipulate [@Fallis2014AF]. We examine reliable and unreliable headline extracted from two domains with widespread misinformation: Covid-19 [@hossain-etal-2020-covidlies] and climate change [@lett8fake]. We additionally test on cancer news [@10.1145/3394486.3403092] to measure out-of-domain performance. + +We retrieve both trustworthy and misinformation headlines related to climate change from NELA-GT-2018-2020 [@gruppi2020nelagt2019; @norregaard2019nelagt2018], a dataset of news articles from 519 sources. Each source in this dataset is labeled with a 3-way trustworthy score (reliable / sometimes reliable / unreliable). We discard articles from "sometimes reliable\" sources since the most appropriate label under a binary labeling scheme is unclear. To identify headlines related to climate change, we use keyword filtering.[^5] We also use claims from the SciDCC dataset [@mishraneuralnere], which consists of 11k real news articles from ScienceDaily,[^6] and Climate-FEVER [@diggelmann2021climatefever], which consists of more than 1,500 true and false climate claims from Wikipedia. We extract claims with either supported or refuted labels in the original dataset.[^7] + +::: {#table:themes} + Theme Climate Covid + ---------------------- --------- ------- + Climate Statistics + Natural Disasters + Entertainment + Ideology + Disease Transmission + Disease Statistics + Health Treatments + Protective Gear + Government Entities + Society + Technology + + : Themes present in articles by each news topic. Some are covered by both climate and Covid domains, while others are domain specific. +::: + +::: {#table:both_stats} + Statistic Train Dev. Test Cancer + ----------------- --------- -------- -------- -------- + Headlines 19,897 2,460 2,133 674 + Unique Intents 38,172 4,867 4,388 1,232 + Unique Percept. 2,609 538 421 174 + Unique Actions 15,036 2,176 1,739 704 + Total Pairs 159,564 19,700 17,890 5,227 + + : Dataset-level breakdown of statistics for MRF corpus. +::: + +[]{#table:both_stats label="table:both_stats"} + +For trustworthy news regarding Covid-19, we use the CoAID dataset [@cui2020coaid] and a Covid-19 related subset of NELA-GT-2020 [@gruppi2020nelagt2019]. CoAID contains 3,565 news headlines from reliable sources. These headlines contain Covid-19 specific keywords and are scraped from nine trustworthy outlets (e.g. the World Health Organization). + +For unreliable news (misinformation), we use The CoronaVirusFacts/DatosCoronaVirus Alliance Database, a dataset of over 10,000 mostly false claims related to Covid-19 and the ESOC Covid-19 Misinformation Dataset, which consists of over 200 additional URLs for (mis/dis)information examples.[^8][^9] These claims originate from social media posts, manipulated media, and news articles, that have been manually reviewed and summarized by fact-checkers. + +We construct an evaluation set for testing out-of-domain performance using cancer real and misinformation headlines from the DETERRENT dataset [@10.1145/3394486.3403092], consisting of 4.6k real news and 1.4k fake news articles. + +In this section we outline the structured annotation interface used to collect the dataset. Statistics for the full dataset are provided in Table [2](#table:both_stats){reference-type="ref" reference="table:both_stats"}. + +We use the Amazon Mechanical Turk (MTurk) crowdsourcing platform.[^10] We provide Figure [\[fig:marsannotation2\]](#fig:marsannotation2){reference-type="ref" reference="fig:marsannotation2"} in the Appendix to show the layout of our annotation task. For ease of readability during annotation, we present a headline summarizing the article to annotators, rather than the full text of the article. Annotators then rate veracity and likelihood of spread based on the headline, as well as providing free-text responses for writer intent, reader perception and reader action.[^11] We structure the annotation framework around the themes described in §[2](#sec:dims){reference-type="ref" reference="sec:dims"}. + +We use a three-stage annotation process for ensuring quality control. In the initial pilot, we select a pool of pre-qualified workers by restricting to workers located in the US who have had at least 99% of their *human intelligence tasks* (hits) approved and have had at least 5000 hits approved. We approved workers who consistently submitted high-quality annotations for the second stage of our data annotation, in which we assessed the ability of workers to discern between misinformation and real news. We removed workers whose accuracy at predicting the label (real/misinfo) of news headlines fell below 70%. Our final pool consists of 80 workers who submitted at least three annotations during the pilot tasks. We achieve pairwise agreement of 79% on the label predicted by annotators during stage 3, which is comparable to prior work on Covid misinformation [@hossain-etal-2020-covidlies]. To account for chance agreement, we also measure Cohen's Kappa $\kappa = .51$, which is considered "moderate" agreement. Additional quality control measures were taken as part of our extensive annotation post-processing. For details, see Appendix [\[sec:postp\]](#sec:postp){reference-type="ref" reference="sec:postp"}. + +We provided an optional demographic survey to MTurk workers during annotation. Of the 63 annotators who reported ethnicity, 82.54% identified as White, 9.52% as Black/African-American, 6.35% as Asian/Pacific Islander, and 1.59% as Hispanic/Latino. For self-identified gender, 59% were male and 41% were female. Annotators were generally well-educated, with 74% reporting having a professional degree, college-level degree or higher. Most annotators were between the ages of 25 and 54 (88%). We also asked annotators for their preferred news sources. New York Times, CNN, Twitter, Washington Post, NPR, Reddit, Reuters, BBC, YouTube and Facebook were reported as the 10 most common news sources. + +We test the ability of large-scale language models to predict Misinfo Reaction Frames. For free-text inferences (e.g. writer intent, reader perception), we use generative language models, specifically T5 encoder-decoder [@Raffel2020ExploringTL] and GPT-2 decoder-only models [@radford2019language]. For categorical inferences (e.g. the gold label), we use either generative models or BERT-based discriminative models [@devlin-etal-2019-bert]. We compare neural models to a simple retrieval baseline (**BERT-NN**) where we use gold implications aligned with the most similar headline from the training set.[^12] + +For generative models, we use the following input sequence $$x = h_1~...~h_T || s_{d} || s_{t},$$ where $h$ is a headline of length $T$ tokens, $s_t \in \{$\[*covid*\]$,$\[*climate*\]$\}$ is a special topic control token, and $s_d$ is a special dimension control token representing one of six reaction frame dimensions. Here $||$ represents concatenation. The output is a short sequence representing the predicted inference (e.g. "*to protest*" for reader action, "*misinfo*" for the gold label). For GPT-2 models we also append the gold output inference $y = g_1~...~g_N$ during training, where $N$ is the length of the inference. + +We predict each token of the output inference starting from the topic token $s_t$ until the \[$eos$\] special token is generated. In the case of data with unknown topic labels, this allows us to jointly predict the topic label and output inference. We decode using beam search, since generations by beam search are known to be less diverse but more factually aligned with the context [@massarelli-etal-2020-decoding]. + +For discriminative models, we use the following input sequence $$x = [CLS] h_1~...~h_T [SEP],$$ where \[CLS\] and \[SEP\] are model-specific special tokens. The output is a categorical inference. + +All our models are optimized using cross-entropy loss, where generally for a sequence of tokens $t$ $$CE(t) = - \frac{1}{|t|}\sum_{i=1}^{|t|} log P_\theta(t_i|t_1,...,t_{i-1}).$$ Here $P_\theta$ is the probability given a particular language model $\theta$. Since GPT-2 does not explicitly distinguish between the input and output (target) sequence during training, we take the loss with respect to the full sequence. For T5 we take the loss with respect only to the output. + +To improve generalization of MRF models, we use an additional masked fine-tuning step. We first train a language model $\theta$ on a set of Covid-19 training examples $D_{covid}$ and climate training examples $D_{climate}$. Then we use the Textrank algorithm [@mihalcea-tarau-2004-textrank] to find salient keyphrases in $D_{covid}$ and $D_{climate}$, which we term $k_{covid}$ and $k_{climate}$ respectively. We determine domain-specific keyphrases by looking at the complement of $k_{covid} \cap k_{climate}$ $$\begin{multline*} + k'_{covid} = k_{covid} \setminus k_{covid} \cap k_{climate} \\ + k'_{climate} = k_{climate} \setminus k_{covid} \cap k_{climate}, +\end{multline*}$$ and only keep the top 100 keyphrases for each domain. We mask out these keyphrases in the training examples from $D_{covid}$ and $D_{climate}$ by replacing them with a $$ token. Then we continue training by fine-tuning on the masked examples. 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\ No newline at end of file diff --git a/2108.12841/main_diagram/main_diagram.pdf b/2108.12841/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f5c97aed3336074a43c79c6c04e55bc486dfa7ea Binary files /dev/null and b/2108.12841/main_diagram/main_diagram.pdf differ diff --git a/2108.12841/paper_text/intro_method.md b/2108.12841/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..5fa9ea55cba363d7d612314c687252caf4217f16 --- /dev/null +++ b/2108.12841/paper_text/intro_method.md @@ -0,0 +1,129 @@ +# Introduction + +Deep neural network has been widely used in many computer vision tasks, yielding significant improvements over conventional approaches since AlexNet [18]. However, image denoising has been one of the tasks in which conventional methods such as BM3D [7] outperformed many early deep learning based ones [5,47,48] until DnCNN [51] outperforms it for synthetic Gaussian noise at the expense of massive amount of noiseless and noisy image pairs [51]. + +Requiring no clean and/or noisy image pairs, deep image prior (DIP) [42, 43] has shown that a randomly initialized network with hour-glass structure acts as a prior for several inverse problems including denoising, super-resolution, and inpainting with a single degraded image. Although DIP exhibits remarkable performance in these inverse problems, denoising is the particular task that DIP does not perform well, *i.e.*, a single run yields far lower PSNR than BM3D even for synthetic Gaussian noise set-up [42, 43]. Further- + +![](_page_0_Figure_12.jpeg) + +![](_page_0_Figure_13.jpeg) + +Figure 1: Comparison of single image based denoising methods. 'L↓' refers to LPIPS and it is lower the better. 'P↑' refers to PSNR and it is higher the better. Our method denoises an image while preserves rich details; showing the best LPIPS with comparable PSNR to Self2Self (S2S) [30]. Ours shows much better trade-off in PSNR and LPIPS than all other methods including all different ensembling attempts of state of the art (S2S). (Numbers in the circle of S2S denotes number of models in ensemble) + +more, for the best performance, one needs to monitor the PSNR (*i.e.*, the ground-truth clean image is required here) and stop the iterations before fitting to noise. Deep Decoder addresses the issue by proposing a strong structural regularization to allow longer iterations for the inverse problems including denoising [15]. However, it yields worse denoising performance than DIP due to low model complexity. + +For better use of DIP for denoising without monitoring PSNR with a clean image, we first analyze the model complexity of the DIP by the notion of effective degrees of freedom (DF) [10, 12, 41]. Specifically, the DF quantifies the amount of overfitting (*i.e.*, optimism) of a chosen hypothesis (*i.e.*, a trained neural network model) to the given training data [10]. In other words, when overfitting occurs, the DF increases. Therefore, to prevent the overfitting of the DIP network to the noise, we want to suppress the DF over + +iterations. But obtaining DF again requires a clean (ground truth) image. Fortunately, for the Gaussian noise model, there are approximations for DF without using a clean image; Monte-Carlo divergence approximations in Stein's unbiased risk estimator (SURE) (Eqs. [8,](#page-3-0) [9\)](#page-3-1) (DFMC ). + +Leveraging SURE and improvement techniques in DIP [\[43\]](#page-9-4), we propose an objective with 'stochastic temporal ensembling (STE),' which mimics ensembling of many noise realizations in a single optimization run. On the proposed objective with the STE, we propose to stop the iteration when the proposed objective function crosses zero. The proposed method leads to much better solutions than DIP and outperforms prior arts for single image denoising. In addition, inspired by PURE formulation [\[20,](#page-8-7) [24\]](#page-8-8), we extend our objective function to address the Poisson noise. + +We empirically validate our method by comparing DIP based prior arts for denoising performance in various metrics that are suggested in the literature [\[13\]](#page-8-9) such as PSNR, SSIM and learned perceptual image patch similarity (LPIPS) [\[54\]](#page-9-6) on seven different datasets. LPIPS has been widely used in super resolution literature to complement PSNR, SSIM to measure the recovery power of details [\[21\]](#page-8-10). Since it is challenging for denoiser to suppress noise and preserve details together [\[4\]](#page-8-11), we argue that LPIPS is another appropriate metric to evaluate denoisers. Note that it has not been widely used in denoising literature yet to analyze the denoising performance. Our method not only denoises the images but also preserves rich textual details, outperforming other methods in LPIPS with comparable classic measures including the PSNR and SSIM. + +Our contributions are summarized as follows: + +- Analyzing the DIP for denoising with effective degrees of freedom (DF) of a network and propose a loss based stopping criterion without ground-truth image. +- Incorporating noise regularization and exponential moving average by the proposed stochastic temporal ensembling (STE) method. +- Diverse evaluation in various metrics such as LPIPS, PSNR and SSIM in seven different datasets. +- Extending our method to Poisson noise. + +# Method + +**Deep image prior (DIP).** Let a noisy image $\mathbf{y} \in \mathbb{R}^N$ be + + + +where $\mathbf{x} \in \mathbb{R}^N$ be a noiseless image that one would like to recover and $\mathbf{n} \in \mathbb{R}^N$ be an *i.i.d.* Gaussian noise such that $\mathbf{n} \sim \mathcal{N}(\mathbf{0}, \sigma^2 \mathbf{I})$ where **I** is an identity matrix. Denoising can be formulated as a problem of predicting the unknown x from known noisy observation y. Ulyanov et al. [43] argued that a network architecture naturally encourages to restore the original image from a degraded image y and name it as deep image prior (DIP). Specifically, DIP optimizes a convolutional neural network h with parameter $\theta$ by a simple least square loss $\mathcal{L}$ as: + + +$$\hat{\boldsymbol{\theta}} = \arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\dot{\mathbf{n}}; \boldsymbol{\theta}), \mathbf{y}), \tag{2}$$ + +where $\dot{\bf n}$ is a random variable that is independent of $\bf v$ . If $\bf h$ has enough capacity (i.e., sufficiently large number of parameters or architecture size) to fit to the noisy image v, the output of model $h(\dot{\mathbf{n}}; \hat{\boldsymbol{\theta}})$ should be equal to $\mathbf{v}$ , which is not desirable. DIP uses the early stopping to obtained the results with best PSNR with clean images. + +**Effective degrees of freedom for DIP.** The effective degrees of freedom [10, 41] quantifies the amount of fitting of a model to training data. We analyze the training of DIP by the effective degrees of freedom (DF) in Eq. 3 as a tool for monitoring overfitting to the given noisy image. the DF for the estimator $h(\cdot)$ of x with input y can be defined as follows [14]: + + +$$DF(\mathbf{h}) = \frac{1}{\sigma^2} \sum_{i=1}^n Cov(\mathbf{h}_i(\cdot), \mathbf{y}_i), \tag{3}$$ + +where $\mathbf{h}(\cdot)$ and $\mathbf{y}$ are a model (e.g., a neural network) and noise image respectively. $\sigma$ is the standard deviation of the noise. $\mathbf{h}_i(\cdot)$ and $\mathbf{v}_i$ indicate the $i^{th}$ element of corresponding vectors. For example, if the input to $\mathbf{h}(\cdot)$ is $\dot{\mathbf{n}}$ and $\mathbf{y}$ is a noisy image, i.e., $h(\dot{n})$ , it is the DF for DIP. Note that $\mathbf{h}(\cdot)$ can take any input and we use $\mathbf{y}$ (instead of $\dot{\mathbf{n}}$ ) for our formulation. + +Interestingly, the DF is closely related to the notion of optimism of an estimator h, which is defined by the difference between test error and train error [14,41] as: + +$$\rho(\mathbf{h}) = \mathbb{E}\left[\mathcal{L}(\tilde{\mathbf{y}}, \mathbf{h}(\cdot)) - \mathcal{L}(\mathbf{y}, \mathbf{h}(\cdot))\right],\tag{4}$$ + +where $\mathcal{L}(\cdot)$ is a mean squared error (MSE) loss, $\tilde{\mathbf{y}}$ is another realization from the model (i.e., with different n in Eq. 1) that is independent of y. In [41], it is shown that $\rho(\mathbf{h}) =$ $2\sum_{i=1}^{n} Cov(\mathbf{h}_{i}(\cdot), \mathbf{y}_{i})$ . Thus, combining with Eq. 3, it is straightforward to show that + +$$2\sigma^2 \cdot \mathrm{DF}(\mathbf{h}) = \rho(\mathbf{h}). \tag{5}$$ + +It is challenging to compute the covariance since $h(\cdot)$ is nonlinear (e.g., a neural network), gradually changing in optimization, and the $\rho(\mathbf{h})$ requires many pairs of noisy and clean (ground-truth) images to compute (note that it is an estimate). Here, we introduce a simple approximated degrees of freedom with a single ground-truth and call it as $DF_{GT}$ . We derive the $DF_{GT}$ as following: + +$$2\sigma^2 \cdot \mathrm{DF}_{GT}(\mathbf{h}) \approx \mathcal{L}(\mathbf{x}, \mathbf{h}(\cdot)) - \mathcal{L}(\mathbf{y}, \mathbf{h}(\cdot)) + \sigma^2$$ + (6) + +We describe a simple proof of the estimation in the supplementary material. + +A large DF implies overfitting to the given input y, which is not desirable. If DIP fits to x, DFGT becomes close to 0. The more the DIP is fitting to y, the larger the DF is. We use the $DF_{GT}$ to analyze the DIP optimization in empirical studies in Sec. 5.1. + +To prevent the overfitting of DIP, we try to suppress the DF (Eq. 3) during the optimization without the access of ground-truth clean image x. In Eq. 3, computing the DF is equivalent to the sum of the covariances for each element of the noise image y and the model output $h(\cdot)$ . There are a number of techniques to simply approximate the covariance computation in statistical learning literature such as AIC [1], BIC [36] and Stein's unbiased risk estimator (SURE) [40]. Both AIC and BIC, however, approximate the DF by counting the number of parameters of a model, so for usual over-parameterized deep neural networks, the approximations based on them could be incorrect [3]. Note that $DF_{GT}$ cannot be used for optimizing model because it needs groud-truth clean image x. + +Here, we propose to use SURE to suppress the DF by deriving the DIP formulation using the Stein's lemma. The Stein's lemma for a multivariate Gaussian vector y is [\[40\]](#page-8-17): + +$$\frac{1}{\sigma^2} \sum_{i=1}^n Cov(\mathbf{h}_i(\mathbf{y}), \mathbf{y}_i) = \mathbb{E}\left[\sum_{i=1}^n \frac{\partial \mathbf{h}_i(\mathbf{y})}{\partial \mathbf{y}_i}\right]. \tag{7}$$ + +It simplifies the computation of DF from the covariances between y and h(y) to the expected partial derivatives at each point, which is well approximated in a number of computationally efficient ways [\[32,](#page-8-20) [39\]](#page-8-29). Note that the SURE which is denoted as η(h(y), y), consists of Eq. [7](#page-3-3) and the DIP loss (Eq. [2\)](#page-2-3) with a modification of its input (from n˙ to y) as: + +$$\eta(\mathbf{h}(\mathbf{y}), \mathbf{y}) = \mathcal{L}(\mathbf{y}, \mathbf{h}(\mathbf{y})) + \underbrace{\frac{2\sigma^2}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_i(\mathbf{y})}{\partial (\mathbf{y})_i}}_{\text{divergence term}} - \sigma^2.$$ +(8) + +While the vanilla DIP loss encourages to fit the output of the model h to noisy image y, Eq. [\(8\)](#page-3-0) encourages to approximately fit it to clean image x without access to the x. + +However, it is still computationally demanding to use Eq. [8](#page-3-0) as a loss for optimization with any gradient based algorithm due to the divergence term [\[32\]](#page-8-20). A Monte-Carlo approximation for Eq. [8](#page-3-0) in [\[32\]](#page-8-20) can be a remedy to the computation cost, but it introduces a hyper-parameter ϵ that has to be selected properly for the best performance on different network architectures and/or datasets. For not requiring to tune the hyper-parameter ϵ, we employed an alternative Monte-Carlo approximation for the divergence term [\[39\]](#page-8-29) as: + +$$\frac{1}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_{i}(\mathbf{y})}{\partial \mathbf{y}_{i}} \approx \frac{1}{N} \tilde{\mathbf{n}}^{T} \mathbf{J}_{\tilde{\mathbf{n}}^{T} \mathbf{h}(\mathbf{y})}, \tag{9}$$ + +where n˜ is a standard normal random vector, *i.e*., n˜ ∼ N (0, I) and the i th element of the Jacobian Jn˜T h(y) is ∂n˜ T h(y; θ)/∂yi . We denote this 'estimated degrees of freedom by Monte-Carlo' by DFMC and will use it to monitor the DIP optimization without using the PSNR with the clean ground truth images (Sec. [4.2\)](#page-4-2). + +To improve the fitting accuracy, DIP suggests several methods including noise regularization, exponential moving average [\[43\]](#page-9-4). We propose 'stochastic temporal ensembling (STE)' for better fitting performance by leveraging these methods to our objective. + +Noise regularization on DIP. DIP shows that adding extra temporal noises to the input n˙ of function h(·) at each iteration improves performance for the inverse problems including image denoising [\[43\]](#page-9-4). It is to add a noise vector γ, with γ ∼ N(0, σ2 γ I) to the input of the function at every iteration of the optimization as: + + +$$\hat{\boldsymbol{\theta}} = \arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\dot{\mathbf{n}} + \gamma; \boldsymbol{\theta}), \mathbf{y}), \tag{10}$$ + +where n˙ is fixed but γ is sampled from Gaussian distribution with zero mean, standard deviation of σγ at every iteration. To estimate the x by Eq. [8,](#page-3-0) we replace the input of the model h(·), n˙ , with noisy image y (from Eq. [3](#page-2-1) to Eq. [7\)](#page-3-3). Interestingly, Eq. [10](#page-3-4) becomes similar to the denoising auto-encoder (DAE), which prevents a model from learning a trivial solution by perturbing output of h [\[46\]](#page-9-13). + +Meanwhile, contractive autoencoder (CAE) [\[33\]](#page-8-30) minimizes the Frobenius norm of the Jacobian and SURE and its variants minimize the trace of the Jacobian (Eq. [9\)](#page-3-1) thus suppresses the DF. Since we assume that the different realizations of noise are independent, the off-diagonal elements of the matrix are zero, CAE is equivalent to SURE in terms of suppressing the DF. Alain *et al*. [\[2\]](#page-8-31) later show that the DAE is a special case of the CAE when σγ → 0. We can rewrite the Eq. [10](#page-3-4) by using CAE formulation as: + +$$\arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\mathbf{y}; \boldsymbol{\theta}), \mathbf{y}) + \sigma_{\gamma}^{2} \left\| \frac{\partial \mathbf{h}(\mathbf{y}; \boldsymbol{\theta})}{\partial \mathbf{y}} \right\|_{F}^{2} + o(\sigma_{\gamma}^{2}), (11)$$ + +when σγ → 0, where o(σ 2 γ ) is a high order error term from Taylor expansion. Thus, solving this optimization problem is equivalent to penalizing increase of DF. Here, the noise level σγ serves as a hyper-parameter for determining performance and it improves performance of DIP by using multiple level of σz at optimization of DIP. Thus, we further proposed to model σγ as a uniform random variable instead of a empirically chosen hyper-parameter such that + + +$$\sigma_{\gamma} \sim \mathcal{U}(0, b).$$ + (12) + +Exponential moving average. DIP further shows that averaging the restored images obtained in the last iterations improves the performance of denoising [\[43\]](#page-9-4), which we refer to as 'exponential moving average (EMA).' It can be thought as an analogy to the effect of ensembling [\[31\]](#page-8-32). + +Stochastic temporal ensembling. Leveraging the noise regularization and the EMA, we propose a method called 'stochastic temporal ensembling (STE)' to improve the fitting performance of DIP loss. Specifically, we modify our formulation (Eq. [8\)](#page-3-0) by allowing two noise observations, y1 for target of MSE loss and y2 for the input of the model, h, instead of one y by setting y1 = y and y2 = y + γ as: + +$$\eta(\mathbf{h}(\mathbf{y_2}), \mathbf{y_1}) = \underbrace{\mathcal{L}(\mathbf{h}(\mathbf{y_2}), \mathbf{y_1})}_{\text{data fidelity}} + \underbrace{\frac{2\sigma^2}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_i(\mathbf{y_2})}{\partial (\mathbf{y_2})_i}}_{\text{regularization}} - \sigma^2, \quad (13)$$ + +where σ is a known noise level of y1 (same as Eq. [1\)](#page-2-2), hi(y2) and (y2)i are the i th element of the vectors of h(y2) and y1, respectively. Interestingly, Eq. [13](#page-3-5) is equivalent to the formulation of extended SURE (eSURE) [\[55\]](#page-9-14), which is shown to be a better unbiased estimator of the MSE with + +![](_page_4_Figure_0.jpeg) + +Figure 2: Illustration of a solution trajectory of ours and DIP. We consider the problem of reconstructing an image x from a degraded measurement y. DIP finds its optimal stopping point (t4) by early stopping. Ours changes DIP's solution trajectory from black to orange whose stopping point (t5) is defined by a loss value (Sec[.4.2\)](#page-4-2) and is close to noiseless solution (x). + +the clean image x. But there are a number of critical differences of ours from [\[55\]](#page-9-14). First, Our method does not require training, while Zhussip *et al*. [\[55\]](#page-9-14) requires training with many noisy images. Because Zhussip *et al*. [\[55\]](#page-9-14) use the fixed instance of γ, there is no effect of regularization from (Eq. [10\)](#page-3-4), which gives reasonable performance gain( See Sec[.5.2\)](#page-5-0). This is our final objective function of DIP that stops automatically by a stopping criterion, described in the following section. + +SURE works well if the model h satisfies the smoothness condition, *i.e*., h admits a well-defined second-order Taylor expansion [\[27,](#page-8-18) [38\]](#page-8-21). While a typical learning based denoiser satisfies this smoothness condition [\[38,](#page-8-21)[55\]](#page-9-14), the DIP network 'fits' to a target image (a noisy image in [\[42,](#page-9-3) [43\]](#page-9-4) and an approximate clean image in our objective) and therefore there is no guarantee that the smoothness condition can be satisfied, especially when it has been converged. + +We observed that the divergence term in our formulation (Eq. [13\)](#page-3-5) increases at early iterations (*i.e*., before convergence) while it starts to diverge to −∞ at later iterations (*i.e*., after convergence). This observation is consistent in all our experiments. Note that this divergence phenomenon was not reported in [\[27\]](#page-8-18) because the DIP network with the SURE loss did not seem to be fully converged to recover the fine details with insufficient number of iterations. Based on this observation for our proposed objective, we propose 'zero crossing stopping criterion' to stop iteration when our objective function (Eq. [13\)](#page-3-5) deviates from zero. + +Solution trajectory. To help understand the difference between our method to DIP in optimization procedure, similar to Fig. 3 in [\[43\]](#page-9-4), we illustrate DIP image restoration trajectory with that of our method in Fig. [2.](#page-4-3) DIP degrades the quality of the restored images by the overfitting. To obtain the solution close to the clean ground truth image, DIP uses early stopping (blue t4). Our formulation has different training trajectory (orange) from DIP (black) and automatically stops the optimization by the zero crossing stopping (orange t4). We argue that the resulting image by our formulation is in general closer to the clean image (blue x) than the solution by DIP, which preserves more high frequency details than the solution by the DIP (Sec. [5.3\)](#page-6-0) thanks to a better target to fit (an approximation of the clean x over a noisy image y and our proposed principled stopping criterion without using ground truth image). We empirically analyze this phenomenon with our proposed DFGT and compare it to DFMC in Sec. [5.1](#page-4-1) and the supplementary material. + +As the SURE is limited to Gaussian noise [\[38\]](#page-8-21), there are several attempts to extend it to other types of noises [\[11,](#page-8-22)[20,](#page-8-7) [35\]](#page-8-23). Here, we extend our formulation to Poisson noise as it is a useful model for noise in low-light condition. We modify our formulation (Eq. [13\)](#page-3-5) to use Poisson unbiased risk estimator (PURE) [\[17,](#page-8-33)[20,](#page-8-7)[24\]](#page-8-8) for Poisson noise as follows: + +$$\mathcal{L}(\mathbf{h}(\mathbf{y}), \mathbf{y}) - \frac{\zeta}{N} \sum_{i=1}^{N} \mathbf{y}_{i} + \frac{2\zeta}{\epsilon N} (\mathbf{\check{n}} \odot \mathbf{y})^{\mathbf{T}} (\mathbf{h}(\mathbf{y} + \dot{\epsilon}\mathbf{\check{n}}) - \mathbf{h}(\mathbf{y}))), \quad (14)$$ + +where ˘n is a k-dimensional binary random variable whose element n˘i takes -1 or 1 with probability 0.5 for each, ϵ˙ is a small positive number and ⊙ is a Hadamard product. We empirically validate the Poisson extension in Sec. [5.4.](#page-6-1) diff --git a/2109.14982/main_diagram/main_diagram.drawio b/2109.14982/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..fd56534b33439a24332f7ee7278a373aef7ffe79 --- /dev/null +++ b/2109.14982/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2109.14982/main_diagram/main_diagram.pdf b/2109.14982/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b5628933090d2585b6d97bf76590fe56b9cac3cb Binary files /dev/null and b/2109.14982/main_diagram/main_diagram.pdf differ diff --git a/2109.14982/paper_text/intro_method.md b/2109.14982/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..1561addd7ed3e030f5339219311071ef56f805c9 --- /dev/null +++ b/2109.14982/paper_text/intro_method.md @@ -0,0 +1,81 @@ +# Introduction + +The progress in sensing technologies has significantly expedited the 3D data acquisition pipelines which in turn has increased the availability of large and diverse 3D datasets. With a single 3D sensing device [@lu2006single], one can capture a target surface and represent it as a 3D object, with point clouds and meshes being the most popular representations. Several applications, ranging from virtual reality and 3D avatar generation [@lattas2020avatarme; @potamias2020learning] to 3D printing and digitization of cultural heritage [@pavlidis2007methods], require such representations. However, generally, a 3D capturing device generates thousands of points per second, making processing, visualization and storage of captured 3D objects a computationally daunting task. Often, raw point sets contain an enormous amount of, not only redundant, but also possibly noisy, points with low visual perceptual importance, which results into an unnecessary increase in the storage costs. Thus processing, rendering and editing applications require the development of efficient simplification methods that discard excessive details and reduce the size of the object, while preserving their significant visual characteristics. + +Point cloud simplification can be described as a process of reducing the levels-of-detail (LOD) so as to minimise the introduced perceptual error [@cignoni1998comparison]. In contrast to sampling methods, the main objective of point cloud simplification, is the removal or the collapse of particular points that does not significantly affect the visual perceptual quality, in a way that the most salient features are preserved in the simplified point cloud [@luebke2001developer]. In this study, we propose a learnable strategy to remove the least perceptually important points without sacrificing the overall structure of the point cloud. As perceptually important features that should be preserved, we consider points with increased surface curvature, that have been shown to highly correlate with the human perceptual system [@lee2005mesh; @lavoue2009local]. + +Traditional simplification methods address the task by attempting to solve an optimization problem that minimizes the visual error of the simplified model. Usually, such optimizations are non-convex with high computational cost, where a point importance queue is constructed to sort the 3D points according to their scores [@rossignac1993multi; @hoppe1996progressive; @garland1997surface]. Point cloud simplification methods can be categorized as *mesh-based* and as *point decimation-based*. Mesh-based methods attempt to reconstruct a 3D surface from the point cloud and simplify the generated mesh. In contrast, point decimation-based methods directly select points from the reference point cloud according to their feature scores. Edge contraction remains to date one of the most successful and popular mesh-based methods, since it produces high quality approximations of the input [@garland1997surface; @garland1998simplifying]. Although several approaches proposed parallel GPU computations to reduce the execution time even by 20 times [@decoro2007real; @wang2019fast], the simplification task can be still considered as a computationally hard problem. To this end, it is essential to reduce the computation and time complexity by leveraging neural networks to efficiently simplify point clouds. + +In this study, we propose the first, to the best of our knowledge, learnable point cloud simplification method. The proposed method preserves both the salient features as well as the overall structure of the input and can be used for real-time point cloud simplification without any prior surface reconstruction. We also show the limitations of popular distance metrics, such as Chamfer and Haussdorf, to capture salient details of the simplified models and we propose several evaluation criteria that are well-suited for simplification tasks. The proposed method is extensively evaluated in a series of wide range experiments. + +The rest of the paper is structured as follows. In Section 2, we succinctly present a summary of related work covering the relevant areas of mesh and point cloud simplification, point cloud sampling as well as learnable graph pooling methods. In Section 3, we present the preliminaries and the details of the proposed methods including the model architecture components, the training procedure, the limitations of uniform distance measures along with the implementation details. Section 4 is dedicated to review and present the evaluation criteria used to measure the performance of the proposed method. Finally, in Section 5, we extensively evaluate our method with a series of qualitative and quantitative experiments. In particular, we report the performance and the execution time of the proposed method using many perceptual and distance measures. In addition, we show that the simplified point clouds can be still identified by pre-trained classifiers. We also qualitatively evaluate the proposed method under noisy and in-the-wild point clouds and establish our findings using a user-study. + +# Method + +Calculating the local surface properties of an unstructured point cloud is a non-trivial problem. As demonstrated in [@hoppe1992surface; @pauly2002efficient], covariance analysis can be an intuitive estimator of the surface normals and curvature. In particular, considering a neighborhood $\mathcal{N}_i$ around the point $\mathbf{p}_i \in \mathcal{R}^3$ we can define the covariance matrix: + +$$\begin{equation} + C =\begin{bmatrix} + \mathbf{p}_{i_1} - \mathbf{p}_i \\ + \mathbf{p}_{i_2} - \mathbf{p}_i\\ + \vdots \\ + \mathbf{p}_{i_k} - \mathbf{p}_i +\end{bmatrix}^T \cdot \begin{bmatrix} + \mathbf{p}_{i_1} - \mathbf{p}_i \\ + \mathbf{p}_{i_2} - \mathbf{p}_i\\ + \vdots \\ + \mathbf{p}_{i_k} - \mathbf{p}_i +\end{bmatrix} +\in \mathbb{R}^{\lvert\mathcal{N}_i\rvert\times\lvert\mathcal{N}_i\lvert} +\end{equation}$$ where $\mathbf{p}_{i_j} \in \mathcal{N}_i$. + +Solving the eigendecomposition of the covariance matrix $C$, we can derive the eigenvectors corresponding to the principal eigenvalues, that define an orthogonal frame at point $\mathbf{p}_i$. The eigenvalues $\lambda_i$ measure the variation along the axis defined by their corresponding eigenvector. Intuitively, the eigenvectors that correspond to the largest eigenvalues span the tangent plane at point $\mathbf{p}_i$, whereas the eigenvector corresponding to the smallest eigenvalue can be used to approximate the surface normal $n_i$. Thus, given that the smallest eigenvalue measures the deviation of point $\mathbf{p}_i$ from the surface, it can be used as an estimate of point curvature. As shown in [@pauly2002efficient], we may define: $$\begin{equation} + \kappa(\mathbf{p}_i) = \frac{\lambda_0}{\lambda_0+\lambda_1+\lambda_2}, \quad \lambda_0<\lambda_1<\lambda_2 +\end{equation}$$ as the local curvature estimate at point $\mathbf{p}_i$ which is ideal for tasks such as point simplification. Using the previously estimated curvature at point $\mathbf{p}_i$ we can estimate the mean curvature as the Gaussian weighted average of the curvatures around the neighborhood $\mathcal{N}_i$: $$\begin{equation} + \bar{\mathcal{K}}(\mathbf{p}_i) = \frac{\sum\limits_{j \in \mathcal{N}_i} \kappa(\mathbf{p}_j) \exp{(-\|\mathbf{p}_j - \mathbf{p}_i\|^2/h})}{\sum\limits_{j \in \mathcal{N}_i}\exp{(-\|\mathbf{p}_j - \mathbf{p}_i\|^2/h}) } +\end{equation}$$ where $h$ is a constant defining the radius of the neighborhood. Finally, we can define an estimation of the roughness as the difference between curvature and the mean curvature at point $\mathbf{p}_i$ as: $$\begin{equation} + \mathcal{R}(\mathbf{p}_i)=\lvert\kappa(\mathbf{p}_i) -\bar{\mathcal{K}}(\mathbf{p}_i)\rvert +\end{equation}$$ + +
+
+ +
+
Point cloud simplified using FPS (left) achieves better Chamfer distance (CD) than a point cloud decimated using curvature-preservation methods (right). However, the perceptual similarity scores are better for the latter.
+
+ +The main building block of our architecture is a graph neural network that receives at its input a point cloud (or a mesh) $\mathcal{P}_1$ with $N$ points $\mathbf{p}_i$ and outputs a simplified version $\mathcal{P}_2$ with $M$ points, 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 \ No newline at end of file diff --git a/2110.06084/paper_text/intro_method.md b/2110.06084/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..919800959b7a4f697023ddb9ef3cdc0cd974ef20 --- /dev/null +++ b/2110.06084/paper_text/intro_method.md @@ -0,0 +1,35 @@ +# Introduction + +In this study, we analyze linear G-CNNs in a binary classification setting, where hidden layers perform equivariant operations over a finite group $G$, and networks have no nonlinear activation function after their linear group operations. Inputs $\vx_i$ are vectors of dimension $|G|$ (*i.e.,* vectorized group functions $\vx:G \to \mathbb{R}$), and targets $y_i$ are scalars taking the value of either $+1$ or $-1$. Hidden layers in our G-CNNs perform cross-correlation over a group $G$, defined as $$\begin{equation} + (\vg \star \vh)(u) = \sum_{v \in G} \vg(uv) \vh(v) + \label{eq:cross-correlation} +\end{equation}$$ where $\vg, \vh:G \to \mathbb{R}$. Note that the above is equivariant to the left action of the group, *i.e.,* if $w \in G$ and $\vg_w'(u) = \vg(wu)$, then $(\vg_w' \star \vh)(u) = (\vg \star \vh)(wu)$. The final layer of our G-CNN is a fully connected layer mapping vectors of length $|G|$ to scalars. We note that this final layer in general will not construct functions that are symmetric to the group operations, as strictly enforcing group invariance in this linear setting will result in trivial outputs (only scalings of the average value of the input). Nonetheless, this model still captures the composed convolutions of G-CNNs, and is similar in construction to many practical G-CNN models, whose earlier G-convolutions still capture useful high-level equivariant features. For instance, the spherical CNN of @cohen2018spherical also has a final fully connected layer. + +Analogous to the discrete Fourier transform, there exists a *group* Fourier transform mapping a function into the Fourier basis over the irreps of $G$. + +![Cross-correlation of two functions over a group is equivalent to matrix multiplication over irreps (shown as blocks of a larger matrix here) in Fourier space.](figures/nonabelian_chungus.pdf){#fig:correlation width="3.3in"} + +::: {#def:group_fourier_transform .definition} +**Definition 1** (Group Fourier transform). Let $f:G \to \mathbb{C}$. Given a fixed ordering of $G$, let $\ve_u$ be the standard basis vector in $\R^{|G|}$ that is $1$ at the location of $u$ and $0$ elsewhere. Then, $\vf = \sum_{u \in G} f(u) \ve_u$ is the vectorized function $\vf$. Given ${}\widehat{G}$ a complete set of unitary irreps of $G$, let $\rho \in {}\widehat{G}$ be a given irrep of dimension $d_\rho$, $\rho:G \longrightarrow \operatorname{GL}\left(d_\rho,\,\mathbb{C}\right)$[^2]. The group Fourier transform of $f$, $\widehat{f}: \widehat{G} \rightarrow \mathbb{C}$ at a representation $\rho$ is defined as [@terras1999fourier] $$\begin{equation} + \widehat{f}(\rho) = \sum_{u \in G} f(u) \rho(u). +\end{equation}$$ By choosing a fixed ordering of $\widehat{G}$, one can similarly construct $\widehat{\vf}$ as a block-diagonal matrix version of $\widehat{f}$ (as in [4](#fig:correlation){reference-type="ref+label" reference="fig:correlation"}). We define $\mathcal{F}_M$ to be the *matrix* Fourier transform that takes $\vf$ to $\widehat{\vf}$: $$\begin{align} +% \cF_M f = \sum_{u \in G} f(u) \cF_M (u) = \sum_{u \in G} f(u) \bigoplus_{\rho \in {}\widehat{G}} \rho(u)^{\oplus d_\rho} = \bigoplus_{\rho \in {}\widehat{G}} \widehat{f}(\rho)^{\oplus d_\rho} \;\in\on{GL}\left(|G|, \,\mathbb{C}\right) +\widehat \vf = \mathcal{F}_M \vf = \bigoplus_{\rho \in {}\widehat{G}} \widehat{f}(\rho)^{\oplus d_\rho} \;\in\operatorname{GL}\left(|G|, \,\mathbb{C}\right). +\end{align}$$ $\widehat \vf$ or $\mathcal{F}_M \vf$ are shortened notation for the complete Fourier transform. Furthermore, by vectorizing the matrix $\widehat{\vf}$, there is a *unitary* matrix $\mathcal{F}$ taking $\vf$ to $\widehat{\vf}$, analogous to the standard discrete Fourier matrix. We use the following explicit construction of $\mathcal{F}$: denoting $\ve_{[\rho,i,j]}$ as the column-major vectorized basis for element $\rho_{ij}$ in the group Fourier transform, then we can form the matrix $$\begin{equation} + \mathcal{F}= \sum_{u \in G} \sum_{\rho \in {}\widehat{G}} \frac{\sqrt{d_\rho}}{\sqrt{|G|}} \sum_{i,j=1}^{d_\rho} \rho(u)_{ij} \ve_{[\rho,i,j]} \ve_u^{T}. +\end{equation}$$ Intuitively, for each group element $g$, the matrix $\mathcal{F}$ contains all the irrep images $\rho(g)$ 'flattened' into a single column. See [9](#app:gfourier){reference-type="ref+label" reference="app:gfourier"} for further exposition. +::: + +Convolution and cross-correlation are equivalent, up to scaling, to matrix operations after Fourier transformation. For example, for cross-correlation ([\[eq:cross-correlation\]](#eq:cross-correlation){reference-type="ref+label" reference="eq:cross-correlation"}), $\widehat{(g \star h)} (\rho) = {}\widehat{g}(\rho) {}\widehat{h}(\rho)^\dagger$. This simple fact, illustrated in [4](#fig:correlation){reference-type="ref+label" reference="fig:correlation"}, is behind the proofs of our implicit bias results. + +
+
+ +
A practical G-CNN architecture for D8.
+
+
+ +
The linear G-CNN architecture we analyze for D8.
+
+
A comparison between a practical G-CNN architecture for the group D8, and its corresponding linear idealization that we will theoretically analyze. D8 is a group of size 8, consisting of all rotations by 90 degrees and reflections. In a practical architecture, as shown in the first panel, the input may be an image, or anything upon which D8 acts; it can be convolved over D8 with respect to a learnable filter, yielding a function on D8 (i.e. a function that takes 8 values). The following layers intersperse D8-convolutions, possibly with several channels, and nonlinearities, before a final fully connected layer yields a scalar output. In contrast, the second panel shows the simplified architecture we analyze here. In particular, there are no non-linearities and only a single channel is used. Furthermore, we assume the input is already a function on D8, i.e. the input is an 8-dimensional vector. (One can think of this input as a fixed featurization of some image convolved over D8 with a fixed filter.)
+
diff --git a/2110.11852/main_diagram/main_diagram.drawio b/2110.11852/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8f7d44534c4658bf7dcda3f16eae021146e622a7 --- /dev/null +++ b/2110.11852/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2110.11852/main_diagram/main_diagram.pdf b/2110.11852/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f3e7d2d43b4127195cd164b8b35331bfd8502bfc Binary files /dev/null and b/2110.11852/main_diagram/main_diagram.pdf differ diff --git a/2110.11852/paper_text/intro_method.md b/2110.11852/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..27039026ed0d98e13541b057c9452b0b195f6bed --- /dev/null +++ b/2110.11852/paper_text/intro_method.md @@ -0,0 +1,75 @@ +# Introduction + +In the literature of time series analysis, AR and ARMA models are most commonly used to fit and to predict time series data. For example, given the historical traffic volume $z_1, z_2, ..., z_{t-1}$ on a specific road, the government wants to predict future traffic $z_t, z_{t+1}, ...$. Generally speaking, each observation $z_t$ can depend on all previous ones in a nonlinear manner and the relationship may vary as $t$ differs. AR and ARMA models are linear and assume a formulation where coefficients only depend on the *time lag* between the target and the past observation, shared across different target timestamp $t$. Thus, they are simple, parsimonious and were proved to be useful in many applications. + +Time series is a sequence of random variables and the white noise sequence plays a necessary role in time series models as the source of randomness. In the main text, we refer to the white noise term as the additive error because it is an addend in the model equation. + +Formally, a $p$th order autoregressive model, a.k.a., an AR($p$) model, $\{Z_t\}$ satisfies $$\begin{equation*} +Z_t = \theta_0 + \phi_1Z_{t-1} + \phi_2Z_{t-2} + \cdots + \phi_pZ_{t-p} + a_t, +\end{equation*}$$ where $p\geq 0$ is an integer, $\phi$'s are real parameters and $\{a_t\}$ is a white noise sequence. An autoregressive-moving-average model of orders $p$ and $q$, a.k.a., an ARMA$(p, q)$ model, satisfies $$\begin{equation*} +Z_t = \theta_0 + \phi_1Z_{t-1} + \phi_2Z_{t-2} + \cdots + \phi_pZ_{t-p} + a_t - \theta_1a_{t-1} - \theta_2 a_{t-1} - \cdots - \theta_q a_{t-q}, +\end{equation*}$$ where $p, q\geq 0$ are integers, $\theta$'s are real parameters for the MA part. *Invertibility* is a property of time series models to characterize whether the information sequence $\{a_t\}$ can be recovered from past observations, and it is always required in time series applications. If an ARMA model is invertible, then it has an *AR representation* $$\begin{equation*} +Z_t = \pi_1 Z_{t-1} + \pi_2 Z_{t-2} + \cdots + a_t , +\end{equation*}$$ which takes the form of an AR($\infty$) model, where the infinitely many $\pi$'s can be fully determined by $p+q$ parameters, i.e., $\phi$'s and $\theta$'s. In the main text, we claim that ARMA model simplifies AR and give an example using ARMA$(1, 1)$; see Eq. (7). We are referring to the fact that any invertible ARMA model has an AR representation, whose parameters can be fully determined by the (much fewer) parameters of the ARMA model. + +
+ +
Visualization of a layer in the Shared-Lag modification of DenseNet in Eq. (9).
+
+ +Figure [4](#fig:shared1x1){reference-type="ref" reference="fig:shared1x1"} visualizes the structure of a modified DenseNet layer following Eq. (9), where cubes represent feature tensors, rectangles are convolutions annotated with kernel size and output channels, and $\oplus$ denotes elementwise addition. Numbers inside dashed circles correspond to the following notes: + +1. Operations inside the large dashed rectangle represents the $2$nd layer in a dense block. + +2. This 1x1 convolution is shared among layers within a stage. + +3. Dense connections are implemented by this storage area of past layers' output, and $0$ is used as a placeholder. + +4. This arrow represents a virtual \"conveyor belt\" indicating that the feature maps are always ordered from the most recent layer to the most distant layer. + +$\textrm{Conv3}^t$ and $\textrm{Conv1}_{0}^t(\cdot)$ in Eq. (9) are represented by the unshared 3x3 and 1x1 convolutions (white rectangles), and a combination of $\textrm{Conv1}_1, \textrm{Conv1}_2, ..., \textrm{Conv1}_{15}$ is represented by the shared 1x1 convolution (colored rectangle with note 2). Feature maps from previous layers are ordered such that Eq. (9) holds. The Shared-Ordinal variant of DenseNet can be similarly implemented by adjusting the ordering (see note 3 above) and disabling the \"conveyor belt\" (see note 4 above). + +Figure 2 in the main paper shows the $L_1$ norm of the weights of the shared 1x1 convolutions. And we observe the following main difference between the two modes of weight sharing: + +- When weights are shared based on lag, *the most recent layer* corresponds to the largest weight, and the $L_1$ norm decays as lag increases. + +- When weights are shared according to the ordinal number of the layers, *layers closer to the input* correspond to larger weights, and the first few layers have similar weights. + +We also claimed that the quick decaying pattern in the plot for Shared-Lag could be exponential decay, and we provide numerical support below. We fit exponential functions to the curves in Figure 2 and use the $R$-squared metric to compare goodness-of-fit. $R^2 \in (0, 1)$ is a metric that can be interpreted as the proportion of variance explained by a model, i.e., a fitted curve. $R^2$ values for the three curves in the Share-Lag subplot are $0.95, 0.86, 0.90$, while that for Shared-Ordinal are $0.77, 0.91, 0.87$, implying poorer fit. + +This subsection shows that the RLA module in Eq. (11) implements layer aggregation as in Eq. (1). And under a linearity assumption, Eq. (11) can also be shown to have the additive form in Eq. (2). + +Recall the formulation of RLA mechanism in Eq. (11), $$\begin{equation*} +h^t = g^t (h^{t-1}, x^{t-1})\hspace{5mm}\text{and}\hspace{5mm} +x^t=f^t (h^{t-1}, x^{t-1}). +\end{equation*}$$ Recursively substitute $h^s = g^s (h^{s-1}, x^{s-1})$ for $s = t-1, t-2, ...$ into the first equation, we have $$\begin{align*} +h^t & = g^t (h^{t-1}, x^{t-1}) \\ +& = g^t (g^{t-1} (h^{t-2}, x^{t-2}), x^{t-1}) \\ +& = \cdots, +\end{align*}$$ which is a function of $x^{t-1}, x^{t-2}, ..., x^0$ and a constant $h^0$. Thus, Eq. (1) is satisfied. + +If we further assume that there exist functions $g_1^s$ and $g_2^s$ such that $g^s$ and $g_1^s$ satisfy $g^s (h^{s-1}, x^{s-1}) = g_1^s (h^{s-1}) + g_2^s (x^{s-1})$ and $g_1^s(u + v) = g_1^s(u) + g_1^s(v)$ for all $s$, we have $$\begin{align*} +h^t & = g_1^t (h^{t-1}) + g_2^t (x^{t-1}) \\ +& = g_1^t (g_1^{t-1} (h^{t-2}) + g_2^{t-1} (x^{t-2})) + g_2^t (x^{t-1}) \\ +& = g_1^t (g_1^{t-1} (h^{t-2})) + g_1^t (g_2^{t-1} (x^{t-2})) + g_2^t (x^{t-1}) \\ +& = \cdots, +\end{align*}$$ which is a summation over transformed $x^{t-1}, x^{t-2}, ..., x^0$ with a constant $g_1^t(g_1^{t-1}(\cdots g_1^1(h^0) ))$. + +# Method + +Consider the RLA module given by Figure 3 with update $$\begin{equation*} +h^t=g_2[g_1(y^t)+h^{t-1}]\hspace{5mm}\text{and}\hspace{5mm} +x^t=y^t+x^{t-1}, +\end{equation*}$$ where $y^t=f_1^t [\text{Concat}(h^{t-1}, x^{t-1})]$ and $g_1, g_2$ are the shared 1x1 and 3x3 convolutions. As discussed in the main text, ResNets have a layer aggregation interpretation, i.e., $x^t$ is an aggregation of the residual information $y^t$. According to the ablation study in Sections 4.4 and [7.4](#sec:ablation_cifar){reference-type="ref" reference="sec:ablation_cifar"}, it is preferable for the RLA module to perform an aggregation of the residual information $y^t$ instead of the already aggregated $x^t$. Thus, in the following, we show that $h^t$ is an aggregation of $y^t$ instead of $x^t$. + +When nonlinearities are ignored, the shared convolution $g_2$ can be distributed to the two terms, i.e., $$\begin{equation*} +h^t = g_2(h^{t-1}) + g_2 \circ g_1(y^t), +\end{equation*}$$ Furthermore, recursively applying the above equation, we have $$\begin{align} +h^t = \, & g_2(h^{t-1}) + g_2 \circ g_1(y^t) \nonumber \\ += \, & g_2(g_2(h^{t-2}) + g_2 \circ g_1(y^{t-1})) + g_2 \circ g_1(y^t) \nonumber \\ += \, & g_2^2(h^{t-2}) + g_2^2 \circ g_1(y^{t-1}) + g_2 \circ g_1(y^t) \nonumber \\ +& \vdots \nonumber \\ += \, & \sum_{k=1}^t g_2^k \circ g_1 (y^{t-k+1}) + g_2^t(h^0), \label{eq:RLA-LA} +\end{align}$$ where $\circ$ denotes the composition of convolution functions, and with a slight abuse of notation, the composition of the same function $k$ times is denoted as its $k$-th power, e.g., $$\begin{matrix} g_2^k = & \underbrace{ g_2 \circ g_2 \circ \cdots \circ g_2 } \\ & k \text{ times} \end{matrix} ,$$ not to be confused with time varying functions where the superscript denotes the time index. Thus, the RLA hidden feature maps $h^t$ are aggregations of previous residual information. + +Moreover, the patterns of layer aggregation in ResNets and the RLA module in Eq. [\[eq:RLA-LA\]](#eq:RLA-LA){reference-type="eqref" reference="eq:RLA-LA"} are very similar to the AR($\infty$) and ARMA($1, 1$) models introduced in Sections 3.2 and [6.2](#sec:time-series){reference-type="ref" reference="sec:time-series"}. Because for the proposed RLA module in Figure 3, the convolutions $g_l^t$ in Eq. (2) are solely determined by the two shared convolutions $g_2$ and $g_1$. diff --git a/2111.14893/main_diagram/main_diagram.drawio b/2111.14893/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..b4789d48a89edb98402b8085f1e4b19894f6dfc4 --- /dev/null +++ b/2111.14893/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2111.14893/paper_text/intro_method.md b/2111.14893/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..4c66dd3da4eebcd49b6d08fdf7e0b783c721cb05 --- /dev/null +++ b/2111.14893/paper_text/intro_method.md @@ -0,0 +1,83 @@ +# Introduction + +With the recent advances in dense prediction computer vision problems [\[17,](#page-8-0) [25,](#page-8-1) [38,](#page-9-0) [43,](#page-9-1) [47,](#page-9-2) [54,](#page-9-3) [57,](#page-9-4) [64,](#page-10-0) [65,](#page-10-1) [68,](#page-10-2) [72,](#page-10-3) [73\]](#page-10-4), where the aim is to produce pixel-level predictions (*e.g*. semantic and instance segmentation, depth estimation), the interest of the community has started to shift towards the more ambitious goal of learning multiple of these problems jointly by multi-task learning (MTL) [\[7\]](#page-8-2). Compared to the standard single task learning (STL) that focuses on learning an individual model for each task, MTL aims at learning a single model for multiple tasks with a better efficiency and generalization tradeoff while sharing information and computational resources across them. + +Recent MTL dense prediction methods broadly focus on designing MTL architectures [\[4,](#page-8-3) [5,](#page-8-4) [19,](#page-8-5) [24,](#page-8-6) [37,](#page-9-5) [39,](#page-9-6) [44,](#page-9-7) [49,](#page-9-8) [58,](#page-9-9) [60,](#page-9-10) [66,](#page-10-5) [76–](#page-10-6)[78\]](#page-10-7) that enable effective sharing of information across tasks and improving the MTL optimization [\[12,](#page-8-7)[13,](#page-8-8)[21,](#page-8-9) [23,](#page-8-10)[30,](#page-8-11)[33,](#page-9-11)[34,](#page-9-12)[37,](#page-9-5)[51,](#page-9-13)[67\]](#page-10-8) to balance the influence of each task- + +![](_page_0_Figure_8.jpeg) + +Figure 1. Multi-task partially supervised learning. We look at the problem of learning multiple tasks from partially annotated data (b) where not all the task labels are available for each image, which generalizes over the standard supervised learning (a) where all task labels are available. We propose a MTL method that employs a shared feature extractor (fϕ) with task-specific heads (hψ) and exploits label correlations between each task pair by mapping them into a *joint pairwise task-space* and penalizing inconsistencies between the provided ground-truth labels and predictions (c). + +specific loss function and to prevent interference between the tasks in training. We refer to [\[59\]](#page-9-14) for a more comprehensive review. One common and strong assumption in these works is that each training image has to be labelled for all the tasks (Fig. [1\(](#page-0-0)a)). There are two main practical limitations to this assumption. First, curating multi-task image datasets (*e.g*. KITTI [\[20\]](#page-8-12) and CityScapes [\[15\]](#page-8-13)) typically involves using multiple sensors to produce ground-truth labels for several tasks, and obtaining all the labels for each image requires very accurate synchronization between the sensors, which is a challenging research problem by itself [\[61\]](#page-9-15). Second, imagine a scenario where one would like to add a new task to an existing image dataset which is already annotated for another task and obtaining the ground-truth labels for the new task requires using a different sensor (*e.g*. depth camera) to the one which is used to capture the original data. In this case, + +labelling the previously recorded images for the new task will not be possible for many visual scenes (*e.g*. uncontrolled outdoor environments). Such real-world scenarios lead to obtaining partially annotated data and thus ask for algorithms that can learn from such data. + +In this paper, we look at a more realistic and general case of the MTL dense prediction problem where not all the task labels are available for each image (Fig. [1\(](#page-0-0)b)) and we call this setting *multi-task partially supervised learning*. In particular, we assume that each image is at least labelled for one task and each task at least has few labelled images and we would like to learn a multi-task model on them. A naive way of learning from such partial supervision is to train a multi-task model only on the available labels (*i.e*. by setting the weight of the corresponding loss function to 0 for the missing task labels). Though, in this setting, the MTL model is trained on all the images thanks to the parameter sharing across the tasks, it cannot extract the task-specific information from the images for the unlabelled tasks. To this end, one can extend existing single-task semi-supervised learning methods to MTL by penalizing the inconsistent predictions of images over multiple perturbations for the unlabelled tasks (*e.g*. [\[14,](#page-8-14)[29,](#page-8-15)[32,](#page-9-16)[36,](#page-9-17)[56\]](#page-9-18)). While this strategy ensures consistent predictions over various perturbations, it does not guarantee consistency across the related tasks. + +An orthogonal information that has recently been used in MTL is cross-task relation [\[40,](#page-9-19)[50,](#page-9-20)[69\]](#page-10-9) which aims at producing consistent predictions across task pairs. Unfortunately existing methods are not directly applicable for learning from partial supervision, as they require either each training image to be labelled with all the task labels [\[50,](#page-9-20) [69\]](#page-10-9) or cross-task relations that can be analytically derived [\[40\]](#page-9-19). In our setting, compared to [\[40,](#page-9-19)[50,](#page-9-20)[69\]](#page-10-9), there are fewer training images available with ground-truth labels of each task pair and thus it is harder to learn the relationship. In addition, unlike [\[40\]](#page-9-19), we focus on the general setting where one task label cannot be accurately obtained from another (*e.g*. from semantic segmentation to depth) and hence learning exact mappings between two task labels is not possible. + +Motivated by these challenges, we propose a MTL approach that shares a feature extractor between tasks and also learns to relate each task pair in a learned *joint pairwise task-space* (illustrated in Fig. [1\(](#page-0-0)c)), which encodes only the shared information between them and does not require the ill-posed problem of recovering labels of one task from another one. There are two challenges to this goal. First, a naive learning of the joint pairwise task-spaces can lead to trivial mappings that take all predictions to the same point such that each task produces artificially consistent encodings with each other. To this end, we regulate learning of each mapping by penalizing its output to retain high-level information about the input image. Second, the computational cost of modelling each task pair relation can get exponentially expensive with the number of tasks. To address this challenge, we use a single encoder network to learn all the pairwise-task mappings, however, dynamically estimate its weights by conditioning them on the target task pair. + +The main contributions of our method are as following. We propose a new and practical setting for multi-task dense prediction problems and a novel MTL model that penalizes cross-task consistencies between pairs of tasks in joint pairwise task-spaces, each encoding the commonalities between pairs, in a computationally efficient manner. We show that our method can be incorporated to several architectures and significantly outperforms the related baselines in three standard multi-task benchmarks. + +# Method + +Let x ∈ R 3×H×W and y t ∈ R Ot×H×W denote an H × W dimensional RGB image and its dense label for task t respectively, where Ot is the number of output channels for task t. Our goal is to learn a function yˆ t for each task t that accurately predicts the ground-truth label y t of previously unseen images. While such a task-specific function can be learned for each task independently, a more efficient design is to share most of the computations across the tasks via a common feature encoder, convolutional neural network fϕ : R 3×H×W → R C×H′×W′ parameterized by ϕ that takes in an image and produces a C feature maps, each with H′×W′ resolution, where typically H′ < H and W′ < W. In this setting, fϕ is followed by multiple task-specific decoders hψt : R C×H′×W′ → R Ot×H×W , each with its own taskspecific weights ψ t that decodes the extracted feature to predict the label for the task t, *i.e*. yˆ t (x) = hψt ◦ fϕ(x) (Fig. [2\(](#page-3-0)a)). + +Let D denote a set of N training images with their corresponding labels for K tasks. Assume that for each training image x, we have ground-truth labels available only for some tasks where we use T and U to store the indices of labeled and unlabelled tasks respectively, where |T | + |U| = K, U = ∅ indicates all labels available for x and T = ∅ indicates no labels available for x. In this paper, we focus on the partially annotated setting, where each image is labelled at least for one task (|T | ≥ 1) and each task at least has few labelled images. + +A naive way of learning yˆ t for each task on the partially annotated data D is to jointly optimize its parameters on the labelled tasks as following: + + +$$\min_{\phi,\psi} \frac{1}{N} \sum_{n=1}^{N} \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t(\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t), \tag{1}$$ + +where n is the image index and L t is the task-specific differentiable loss function. We denote this setting as the (vanilla) MTL. Here, thanks to the parameter sharing through the feature extractor, its task-agnostic weights are learned on all the images. However, the task-specific weights ψ t are trained only on the labeled images. + +A common strategy to exploit such information from unlabeled tasks is to formulate the problem in a semisupervised learning (SSL) setting. Recent successful SSL techniques [\[2,](#page-8-21)[53\]](#page-9-27) focus on learning models that can produce consistent predictions for unlabelled images when its input is perturbed in various ways. + + +$$\min_{\phi,\psi} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t(\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t) + \frac{1}{|\mathcal{U}_n|} \sum_{t \in \mathcal{U}} L_u(e_r(\hat{y}^t(\boldsymbol{x}_n)), \hat{y}^t(e_r(\boldsymbol{x}_n))) \right), \tag{2}$$ + +where Lu is the unsupervised loss function and er is a geometric transformation (*i.e*. cropping) parameterized by the random variable r (*i.e*. bounding box location). In words, for the unsupervised part, we apply our model to the original input x and also its cropped version er(x), and then we also crop the prediction corresponding to the original input er(ˆy t (xn)) before we measure the difference between two by using Lu. Note that we are aware of more sophisticated task-specific SSL methods for semantic segmentation [\[42,](#page-9-28) [45\]](#page-9-29), depth estimation [\[22,](#page-8-22) [31\]](#page-8-23), however, combining them for multiple tasks, each with different network designs and learning formulations is not trivial and here we focus on one SSL strategy that uses one perturbation type (*i.e*. random cropping) and Lu (*i.e*. mean square error) can be applied to several tasks. + +While optimizing Eq. [\(2\)](#page-2-0) allows learning both taskagnostic and task-specific weights on the labeled and unlabelled data, it does not leverage cross-task relations, which can be used to further supervise unlabelled tasks. Prior works [\[40,](#page-9-19) [69\]](#page-10-9) define the cross-task relations by a mapping function mst for each task-pair (s, t) which maps the prediction for the source task s to target task t labels. The mapping function in [\[40\]](#page-9-19) is analytical based on the assumption that target task labels can be analytically computed from source labels. While such analytical relations is possible + +![](_page_3_Figure_0.jpeg) + +Figure 2. Illustration of our method for multi-task partially supervised learning. Given an image, our method uses a shared feature extractor $f_{\phi}$ taking in the input image and task-specific decoders $(h_{\psi^s}$ and $h_{\psi^t})$ to produce predictions for all tasks (a). We compute the supervised loss $L_t$ for labelled task. Besides, we regularize the cross-task consistency $L_{ct}$ between the unlabelled task's prediction $\hat{y}^s$ and the labelled task's ground-truth $y^t$ in a joint space for the unlabelled task (b). To learn the cross-task consistency efficiently, we propose to use a shared mapping function whose output is conditioned on the task-pair (c) and regularize the learning of mapping function using the feature from $f_{\phi}$ to prevent trivial solution. + +only for certain task pairs, each mapping function in [69] is parameterized by a deep network and its weights are learned by minimizing $L_{ct}(m^{s \to t}(\boldsymbol{y}^s), \boldsymbol{y}^t)$ , where $L_{ct}$ is cross-task function that measures the distance between the mapped source labels and target labels. There are two limitations to this method in our setting. First the training set has limited labelled number of images for both source and target tasks $(\boldsymbol{y}^s)$ and $\boldsymbol{y}^t$ . Second learning such pairwise mappings accurately is not often possible in our case, as the labels of one task can only be partially recovered from another task (e.g.) semantic segmentation to depth estimation). Note that this ill-posed problem can be solved accurately when strong prior knowledge about the data is available. + +To employ cross-task consistency to our setting, we map each task pair (s,t) to a lower-dimensional joint pairwise task-space where only the common features of both tasks are encoded (Fig. 2(b)). Formally, each pairwise task-space for (s,t) is defined by a pair of mapping functions, $m_{\vartheta_s^{st}}: \mathbb{R}^{O^s \times H \times W} \to \mathbb{R}^D$ and $m_{\vartheta_t^{st}}: \mathbb{R}^{O^t \times H \times W} \to \mathbb{R}^D$ parameterized by $\vartheta_s^{st}$ and $\vartheta_t^{st}$ respectively. The cross-task consistency can be incorporated to Eq. (1) as following: + +$$\min_{\phi, \psi, \vartheta} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t (\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t) + \frac{1}{|\mathcal{U}_n|} \sum_{s \in \mathcal{U}_n, t \in \mathcal{T}_n} L_{ct} (m_{\vartheta_s^{st}} (\hat{y}^s(\boldsymbol{x}_n)), m_{\vartheta_t^{st}} (\boldsymbol{y}_n^t)) \right),$$ +(6) + +where $L_{ct}$ is cosine distance (i.e. $L_{ct}(\mathbf{a}, \mathbf{b}) = 1 - (\mathbf{a} \cdot \mathbf{b})/(|\mathbf{a}||\mathbf{b}|)$ ). In words, along with the MTL optimization, Eq. (3) minimizes the cosine distance between the embeddings of the unlabelled task prediction $\hat{y}_s$ and the annotated task label $y^t$ in the joint pairwise task space. Here $m_{\vartheta_s^t}$ + +and $m_{\vartheta_t^{st}}$ are not necessarily equal to allow for treating the mapping from predicted and ground-truth labels differently. Note that one can also include the semi-supervised term $L_u$ in Eq. (3). However we empirically found that it does not bring any tangible performance gain when used with the cross-task term $L_{ct}$ . + +There are two challenges to learn non-trivial pairwise mapping functions in a computationally efficient way. First the number of pairwise mappings to learn quadratically grows with the number of tasks. Although the mapping functions are only used in training, it can still be computationally expensive to train many of them jointly. In addition, learning an accurate mapping for each task-pair can be challenging in case of limited labels. Second the mapping functions can simply learn a trivial solution such that each task is mapped to a fixed point (*e.g.* zero vector) in the joint space. + +Conditional joint task-pair mapping. To address the first challenge, as shown in Fig. 2(c), we propose to use a task-agnostic mapping function $\bar{m}_{\vartheta}$ with one set of parameters $\vartheta$ whose output is conditioned both on the input task (s or t) and task-pair (s, t) through an auxiliary network $(a_{\theta})$ . Concretely, let A denote a variable that includes the input task (s or t) and target pair (s,t) for a pairwise mapping which in practice we encode with an asymmetric $K \times K$ dimensional matrix by setting the corresponding entry to 1 (i.e. A[s,t] = 1 or A[t,s] = 1) and the other entries to 0. Note that the diagonal entries are always zero, as we do not define any self-task relation. Let $\bar{m}_{\vartheta}$ be a multi-layer network and $h_i$ denote a M channel feature map of its i-th layer for which the auxiliary network $a_{\theta}$ , parameterized by $\theta$ , takes in A and outputs two M-dimensional vectors $a_{\theta,i}^c$ and $a_{\theta,i}^b$ . These vectors are applied to transform the feature + +map $h_i$ in a similar way to [46] as following: + +$$\boldsymbol{h}_i \leftarrow a_{\theta,i}^c(A) \odot \boldsymbol{h}_i + a_{\theta,i}^b(A)$$ + +where $\odot$ denote a Hadamard product. In words, the auxiliary network alters the output of the task-agnostic mapping function $\bar{m}_{\vartheta}$ based on A. For brevity, we denote the conditional mapping from s to (s,t) as $m^{s \to st}$ which is a function of $\bar{m}_{\vartheta}$ and $a_{\theta}$ and hence parameterized with $\vartheta$ and $\theta$ . + +We implement each $a_i^c$ and $a_i^b$ as an one layer fully-connected network. Hence, given the light-weight auxiliary network, the computational load for computing the conditional mapping function, in practice, does not vary with the number of task-pairs. Finally, as the dimensionality of each task label vary -e.g. while $O^t$ is 1 for depth estimation and $O^t$ equals to number of categories in semantic segmentation –, we use task-specific input layers and pass each prediction to the corresponding one before feeding it to the joint pairwise task mapping. In the formulation, we include these layers in our mapping $\bar{m}_\vartheta$ and explain their implementation details in Sec. 4. + +**Regularizing mapping function.** To avoid learning trivial mappings, we propose a regularization strategy (Fig. 2) that encourages the mapping to retain high-level information about the input image. To this end, we penalize the distance between the output of the mapping function and a feature vector that is extracted from the input image. In particular, we use the output of the task-agnostic feature extractor $f_{\phi}(x)$ in the regularization. Now we can add the regularizer to the formulation in Eq. (3): + + +$$\min_{\phi,\psi,\vartheta,\theta} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_{n}|} \sum_{t \in \mathcal{T}_{n}} L^{t}(\hat{y}^{t}(\boldsymbol{x}_{n}), \boldsymbol{y}_{n}^{t}) + \frac{1}{|\mathcal{U}_{n}|} \sum_{s \in \mathcal{U}_{n}, t \in \mathcal{T}_{n}} L_{ct}(m^{s \to st}(\hat{y}^{s}(\boldsymbol{x}_{n})), m^{t \to st}(\boldsymbol{y}_{n}^{t})) + R(f_{\phi}(\boldsymbol{x}_{n}), m^{s \to st}(\hat{y}_{s}(\boldsymbol{x}_{n}))) + R(f_{\phi}(\boldsymbol{x}_{n}), m^{t \to st}(\boldsymbol{y}_{n}^{t})) \right), \tag{4}$$ + +where $f_{\phi}(x)$ is the feature from feature encoder $f_{\phi}$ , R is the loss function and we use the cosine similarity loss for R in this work. + +Alternative mapping strategies. Here we discuss two different mapping strategies to exploit cross-task consistency proposed in [69] and their adoption to our setting. As both require learning a mapping from one task's groundtruth label to another one and we have either no or few images with both groundtruth labels, here we approximate them by learning mappings from prediction of one task to another task's groundtruth. In the first case, one can substitute our cross-consistency loss and regularization terms with $L_{ct}(m^{s \to t}(\hat{y}^s(\boldsymbol{x})), \boldsymbol{y}^t)$ in Eq. (4), which is denoted as Direct-Map. In the second case, we replace our + +terms with $L_{ct}(m^{s \to t}(\hat{y}^s(\boldsymbol{x})), m^{s \to t}(\boldsymbol{y}^s))$ that maps both the groundtruth $\boldsymbol{y}^s$ and predicted labels $\hat{\boldsymbol{y}}^s$ and minimize their distance in task t's label space. We denote this setting as Perceptual-Map and compare to them in Sec. 4. + +Alternative loss and regularization strategies. Alternatively, our cross-consistency loss and regularization terms can be replaced with another loss function only that does not allow for learning of trivial mappings. One such loss function is contrastive loss where one can define the predictions for two tasks on the same image as a positive pair (i.e. $m^{s\to st}(\hat{y}^s(\boldsymbol{x}_i))$ and $m^{t\to st}(\boldsymbol{y}_i^t)$ and on different images as a negative pair (i.e. $m^{s \to st}(\hat{y}^s(\boldsymbol{x}_i))$ and $m^{t \to st}(\boldsymbol{y}_i^t)$ ), and penalize when the distance from the positive one is bigger than the negative one. We denote this setting as *Contrastive-Loss*. Another method which also employs positive and negative pairs involves using a discriminator network. The discriminator (a convolutional neural network) takes in positive and negative pairs and predicts their binary labels, while the parameters of the MTL network and mapping functions are alternatively optimized. We denote this setting as Discriminator-Loss and compare to the alternative methods in Sec. 4. diff --git a/2112.08609/main_diagram/main_diagram.drawio b/2112.08609/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e7f88b94b8345e486f89ea0338719a722809e540 --- /dev/null +++ b/2112.08609/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2112.08609/main_diagram/main_diagram.pdf b/2112.08609/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8a696667f698b319a3ec98422e3d225a09c1fdf3 Binary files /dev/null and b/2112.08609/main_diagram/main_diagram.pdf differ diff --git a/2112.08609/paper_text/intro_method.md b/2112.08609/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..7dd5521f2b9f49508ceb4ea83b392ac88684cd6a --- /dev/null +++ b/2112.08609/paper_text/intro_method.md @@ -0,0 +1,163 @@ +# Introduction + +The task of *Question Matching (QM)* aims to identify the question pairs that have the same meaning, and it has been widely used in many applications, e.g., community question answering and intelligent customer services, etc. Though neural QM models have shown compelling performance on various datasets, including Quora Question Pairs (QQP) (Iyer et al., 2017), LCQMC (Liu et al., 2018), BQ (Chen et al., 2018) and AFQMC1, neural models are often not robust to adversarial examples, which means that the neural models predict unexpected outputs given just a small perturbations + +on the inputs. As the example 1 in Tab. 1 shows, a model might not distinguish the minor difference ("面 noodles") between the two sentences, and thus predicts the two questions semantically equivalent. + +Recently, it attracts a lot of attentions from the research community to deal with the robustness issues of neural models on various NLP tasks, such as question matching, natural language inference and machine reading comprehension. Early works examine the robustness of neural models by creating a certain types of artificial adversarial examples (Jia and Liang, 2017; Alzantot et al., 2018; Ren et al., 2019; Jin et al., 2020), and involving human-and-model-in-the-loop to create dynamic adversarial examples (Nie et al., 2020; Wallace et al., 2019). Further studies discover that a few types of superficial cues (i.e. shortcuts) in the training data, are learned by the models and hence affect the model robustness (Gururangan et al., 2018; Mc-Coy et al., 2019; Lai et al., 2021). Besides, several studies try to improve the robustness of the neural models by adversarial data augmentation (Min et al., 2020) and data filtering (Bras et al., 2020). All these efforts lead us to better find and fix the robustness issues to some extends. + +However, there are several limitations in previous studies. First, the analysis and evaluation in previous work focus on just one or a few types of adversarial examples or shortcuts, but we need normative evaluation (Linzen, 2020; Ettinger, 2020; Phang et al.). The goal of the normative evaluation is not to fool a system by exploiting its particular weaknesses, but using systemically controlled datasets to comprehensively evaluate the basic linguistic capabilities of the models in a diverse way. Checklist (Ribeiro et al., 2020) and Textflint (Wang et al., 2021) are great attempts of normative evaluation. However, it is not clear that if the effects of the artificial adversarial methods on artificial examples are still shown on natural texts from real-world applications (Morris et al., 2020). Some other works + +\* Equal contribution. The work was done when Hongyu Zhu was doing internship at Baidu. + +&lt;sup>1It is from Ant Technology Exploration Conference (ATEC) Developer competition, which is no longer available. + +manually perturb the examples to construct natural examples, but the manual perturbations is time consuming and costly [\(Gardner et al.,](#page-8-6) [2020\)](#page-8-6). Moreover, to the best of our knowledge, there are few Chinese datasets for QM robustness evaluation. + +Towards this end, we create a open-domain Chinese dataset namely DuQM contains natural questions with linguistic perturbation for evaluating the robustness of QM models. (1) By *linguistic*, we mean this systematically controlled dataset provides a detailed breakdown of evaluation by linguistic phenomenon. As shown in Tab. [1,](#page-2-0) there are 3 categories and 13 subcategories with 32 linguistic perturbation in DuQM, which enables us to evaluate the model performance by each category instead of just a single metric. (2) By *natural*, we mean all the questions in DuQM are natural and issued by the users in a commercial search engine. This design can help us to properly evaluate the progress of a model's robustness on natural texts rather than artificial texts which may not preserve semantics and introduce grammatical errors. + +The contributions of this paper can be summarized as follows: + +- We construct a Chinese dataset namely DuQM that contains linguistically perturbed natural questions from a commercial search engine. It is a systemically controlled dataset to test the basic linguistic capabilities of the models in a diverse way. (see Sec. [2](#page-1-0) and Sec. [3\)](#page-4-0) +- Our experimental results show that 3 characteristics of DuQM: (1) DuQM is challenging, and has better discrimination power to distinguish the models that perform comparably on other datasets (see Sec. [4.2\)](#page-6-0). (2) The detailed breakdown of evaluation by linguistic phenomena in DuQM helps diagnose the advantages and disadvantages of different models (see Sec. [4.3\)](#page-6-1). (3) Extensive experiment shows that the effect of artificial adversarial examples does not work on natural texts of DuQM. DuQM can help us properly evaluate the models' robustness. (see Sec. [4.4\)](#page-7-0). + +The remaining of this paper is organized as follows. Sec. [2](#page-1-0) describes the 3 categories and 13 subcategories with 32 linguistic perturbation in DuQM. Sec. [3](#page-4-0) gives the construction process of DuQM. In Sec. [4,](#page-5-0) we conduct experiments to demonstrate 3 characteristics of DuQM. We conclude our work in Sec. [5.](#page-8-7) + +The design of DuQM is aimed at a detailed breakdown of evaluation by linguistic phenomenon. Hence, we create DuQM by introducing a set of linguistic features that we believe are important for model diagnosis in terms of linguistic capabilities. Basically, 3 categories of linguistic features are used to build DuQM, i.e., lexical features (see Sec. [2.1\)](#page-1-1), syntactic features (see Sec. [2.2\)](#page-3-0), and pragmatic features (see Sec. [2.3\)](#page-4-1). We list 3 categories, 13 subcategories with 32 operations of perturbation in Tab. [1.](#page-2-0) The detailed descriptions of all categories are given in this section. + +Lexical features are associated with vocabulary items, i.e. words. As a word is the smallest independent but meaningful unit of speech , an operation on a single word may change the meaning of the entire sentence. It is a basic but crucial capability for models to understand word and perceive word-level perturbations. To provide a fine-grained evaluation for model's capability of lexical understanding, we further consider 6 subcategories: + +Part of Speech. Parts of speech (POS), or word classes, describe the part a word plays in a sentence. DuQM considers 6 POS in Chinese grammar, including noun, verb, adjective, adverb, numeral and quantifier, which are content words that carry most meaning of a sentence. In this subcategory, we aim to test the models' understanding of related but not identical words with different POSs. As the example 1 in Tab. [1](#page-2-0) [2](#page-1-2) shows, inserting only one noun "面 *noodles*" makes the sentence meaning different. Furthermore, in this subcategory we provides a set of examples focusing on phrase-level perturbations to check model's capability on understanding word groups that act collectively as a single part of speech (see example 11). + +Named Entity. Different from common nouns that refer to generic things, a named entity (NE) is a proper noun which refers to a specific realworld object. The close relation to world knowledge makes NE ideal for observing models' understanding of the meaning of names and background knowledge about entities. In DuQM, we include *Named Entity* an independent subcategory to test the model's behavior of named entity recognition, and focus on 4 types of NE most commonly seen, + +2All examples discussed in this section are presented in Column *Example and Translation* of Tab. [1.](#page-2-0) + + + +| Category | Subcategory | Perturbation
Operation | Label
#Y / #N | BERT
base | ERNIE
base | RoBERTa
base | MacBERT
base | RoBERTa
large | MacBERT
large | Examples and Translation | +|-------------------|---------------------------------|---------------------------|------------------|--------------|--------------------|-----------------|-----------------|------------------|------------------|-------------------------------------------------------------------------------------------------------------| +| | | insert n. | -/539 | 41.4±3.4 | 40.8±2.1 | 43.0±0.7 | 41.4±2.5 | 45.4±4.1 | 37.3±2.4 | E1: 鸡蛋怎么炒好吃 /鸡蛋 面 怎么炒好吃
how to fry eggs / how to fry egg noodles | +| | | insert v. | -/131 | | 39.4±0.4 33.8±2.6 | 37.4±2.0 | 35.9±2.7 | 39.9±3.1 | 29.5±3.8 | E2: 梦到西红柿 / 梦到 摘 西红柿
dream of tomatoes / dream of picking tomatoes | +| | | insert adj. | -/458 | 23.5±1.9 | 19.2±3.7 | 26.9±4.4 | 23.9±4.2 | 18.1±2.4 | 10.4±2.1 | E3: 有哪些类型的app / 有哪些类型的 移动 app
what are types of apps / what are types of mobile apps | +| | | insert adv. | -/302 | 3.7±0.5 | 4.2±0.5 | 3.8±0.6 | 4.4±1.2 | 5.8±1.5 | 3.1±1.1 | E4: 为什么打嗝 / 为什么 老 打嗝
why burp / why always burp | +| | Part
of | replace n. | -/702 | 86.6±0.3 | 86.7±0.1 | 88.3±0.3 | 88.8±1.2 | 89.4±1.6 | 87.8±0.7 | E5: 申请美国 绿卡 流程 /申请美国 签证 流程
U.S. green card application process / U.S. visa application process | +| | Speech | replace v. | -/466 | 71.7±1.1 | 77.6±0.8 | 76.9±0.4 | 76.5±1.2 | 81.0±1.6 | 81.5±2.2 | E6: 为什么 下蹲 膝盖疼 /为什么 下跪 膝盖疼
why knee pain when squatting / why knee pain when kneeling | +| | | replace adj. | -/472 | 74.3±2.1 | 80.0±1.0 | 77.6±0.7 | 81.6±0.5 | 82.7±1.1 | 82.7±1.6 | E7: 耳朵出血 严重 吗 / 耳朵出血 正常 吗
is the ear bleeding serious / is the ear bleeding normal | +| | | replace adv. | -/188 | 19.1±6.1 | 19.3±4.4 | 16.3±3.8 | 23.9±4.6 | 59.0±4.0 | 56.2±2.0 | E8: 为什么会 经常 头晕 /为什么会 有点 头晕
why regularly feel dizzy / why slightly feel dizzy | +| | | replace num. | -/1116 | 83.2±1.4 | 91.4±0.4 | 85.9±1.8 | 87.2±0.9 | 88.1±0.5 | 91.9±1.1 | E9: 血压 130 /100高吗 / 血压 120 /100高吗
is blood pressure 130 /100 high / is blood pressure 120 /100 high | +| | | replace quantifier | -/22 | 30.3±6.9 | 25.7±5.2 | 33.3±2.6 | 34.9±2.6 | 27.3±0.0 | | 34.8±10.5 E10: 一 束 花多少钱 /一 枝 花多少钱
how much is a bunch of flower / how much is a flower | +| | | replace phrases | -/197 | | 98.0±0.0 98.1±0.2 | 96.6±0.3 | 97.8±0.5 | 97.8±0.2 | 97.5±0 | E11: 如何 提高自己的记忆力 / 如何 增加自己的实力
how to improve my memory / how to increase my strength | +| Lexical Feature | | replace loc. | -/458 | | 96.0±0.6 95.7±0.2 | 95.4±0.4 | 95.0±0.4 | 94.7±0.4 | 94.5±0.5 | E12: 山西 春节习俗 /陕西 春节习俗
Shanxi spring festival customs / Shannxi spring festival customs | +| | Named | replace org. | -/264 | | 94.9±0.2 94.3±0.6 | 91.2±1.4 | 93.4±0.7 | 93.5±0.3 | 93.8±0.1 | E13: 北京邮电大学 附近酒店 /南京邮电大学 附近酒店
hotels near BUPT / hotels near NJUPT | +| | Entity | replace person | -/468 | 90.3±1.3 | 91.0±0.9 | 88.7±1.6 | 91.4±1.6 | 92.3±1.3 | 93.2±1.1 | E14: 陈龙 的妻子 /成龙 的妻子
wife of Long Chen / wife of Jackie Chan | +| | | replace product | -/170 | 83.7±2.6 | 88.2±2.1 | 82.4±6.9 | 83.3±0.3 | 86.0±1.7 | 88.8±4.4 | E15: iphone 6 多少钱 /iphone6x 多少钱
how much is iphone 6 / how much is iphone6x | +| | Synonym | replace n. | 405/- | 51.1±1.1 | 59.7±1.3 | 59.7±2.2 | 60.7±2.0 | 63.3±3.1 | 71.6±4.0 | E16: 猕猴桃 的功效 /奇异果 的功效
health benefits of Chinese gooseberry / health benefits of Kiwi | +| | | replace v. | 372/- | 80.0±0.9 | 81.1±1.6 | 82.5±0.0 | 83.2±1.2 | 84.0±2.0 | 88.1±1.4 | E17: 什么果汁可以 减肥 / 什么果汁可以 减重
what juice can lose weight / what juice can slim | +| | | replace adj. | 453/- | 75.7±1.3 | 77.3±1.1 | 78.8±2.5 | 74.8±0.5 | 79.4±3.4 | 88.5±1.3 | E18: 有趣 搞笑的广告词 / 幽默 搞笑的广告词
funny advertising words / humerous advertising words | +| | | replace adv. | 26/- | | 98.7±2.1 100.0±0.0 | 100.0±0.0 | 100.0±0.0 | 100±0.0 | | 100.0±0.0 E19: 总是 想睡觉是为什么 /老是 想睡觉是为什么
why always want to sleep / why repeatedly want to sleep | +| | Antonym | replace adj. | -/305 | 50.6±3.4 | 69.6±2.9 | 65.0±1.5 | 73.1±4.3 | 91.7±2.3 | 90.7±2.3 | E20: 什么水果脂肪 低 / 什么水果脂肪 高
what fruit is low in fat / what fruit is high in fat | +| | | negate v. | -/153 | 69.9±9.6 | 88.9±1.3 | 84.8±2.9 | 93.3±1.3 | 88.4±0.9 | 91.4±3.4 | E21: 为什么宝宝哭 /为什么宝宝 不 哭
why baby cries / why baby doesn't cry | +| | Negation | negate adj. | -/139 | 73.1±8.5 | 84.2±1.2 | 82.7±1.4 | 88.0±1.5 | 88.0±2.9 | 89.4±1.0 | E22: 为什么苹果是红的 /为什么苹果 不是 红的
why apple is red / why apple is not red | +| | | neg.+antonym | 59/- | 29.9±2.5 | 34.4±2.5 | 39.0±1.7 | 31.1±2.5 | 40.7±1.7 | 53.6±0.9 | E23: 激动 怎么办 /无法 平静 怎么办
what to do if too excited / what to do if can't calm down | +| | Temporal | insert | -/120 | 26.6±2.1 | 29.1±2.1 | 33.1±0.9 | 41.7±3.3 | 47.5±5.4 | 33.6±8.5 | E24: 北京会下雨吗 /北京 明天 会下雨吗
will it rain in Beijing / will it rain in Beijing tomorrow | +| | word | replace | -/114 | 44.1±6.1 | 67.8±2.6 | 55.0±0.5 | 53.8±1.3 | 70.4±6.1 | 78.6±5.8 | E25: 昨天 下雪 了 吗 /明儿 会下雪吗
was it snow yesterday / will it snow tomorrow | +| | Symmetry | swap | 533/- | | 97.3±0.4 98.0±0.1 | 95.2±1.7 | 95.9±0.7 | 93.3±0.9 | 92.5±1.9 | E26: 鱼 和 鸡蛋 能一起吃吗 / 鸡蛋 和 鱼 能一起吃吗
can I eat fish with egg / can I eat egg with fish | +| | Asymmetry | swap | -/497 | 14.5±2.0 | 18.3±3.7 | 26.8±3.2 | 26.4±2.5 | 52.0±4.6 | | 49.1±10.8 E27: 北京 到 上海 航班 /上海 到 北京 航班
Beijing to Shanghai flights / Shanghai to Beijing flights | +| Syntactic Feature | Negative
Asymmetry | swap + negate | 49/- | | 47.6±3.4 37.4±7.7 | 44.2±1.1 | 25.8±3.1 | 23.1±6.7 | 29.9±1.9 | E28: 男人 比 女人 更 高 吗 / 女人 比 男人 更 矮 吗
are men taller than women / are women shorter than men | +| | Voice | insert passive word | 94/37 | 76.8±1.4 | 72.5±0.0 | 77.4±0.9 | 74.0±0.7 | 85.2±1.4 | 74.8±2.2 | E29: 梦见狗咬左腿 /梦见 被 狗咬左腿
dreamed of being bitten by a dog / dreamed of being bitten by a dog | +| | Misspelling | replace | 468/- | | 68.0±2.0 65.1±0.2 | 64.2±0.6 | 65.0±2.3 | 63.5±1.8 | 63.2±1.6 | E30: 什么 纹身 适合我 / 什么 文身 适合我
what tattoo suits me / what tatoo suits me | +| Pragmatic Feature | Discourse Particle
(Simple) | insert or replace | 213/- | 98.7±0.5 | 98.4±0.2 | 98.6±0.5 | 99.2±0.2 | 99.5±0.0 | 99.8±0.2 | E31: 人为什么做梦 /那么 人为什么做梦
why people dream / so why people dream | +| | Discourse Particle
(Complex) | insert or replace | 131/- | 46.5±0.6 | 56.2±2.0 | 64.1±2.0 | 61.6±1.6 | 65.1±3.4 | 68.4±0.3 | E32: 附近最好的餐厅 / 求助我旁边 哪家餐厅 最好吃 ?
best restaurant nearby / heeelp!!! which restaurant is best in my area ? | +| Total | 13 | 32 | 2803/7318 - | | | | | | | - | + +Table 1: Categories of DuQM (described in Sec. [2\)](#page-1-0) and performance of 6 models on DuQM (discussed in Sec. [4\)](#page-5-0). Bold face and underlined indicate the first and second highest accuracy for each testing scenario. + +i.e., location, organization, person and product. Example 12 is a search query and its perturbation on NE. The two named entities, "山西 *Shanxi*" and "陕西 *Shaanxi*", are similar at character level but denote two different locations. We expect that the models can capture the subtle difference. + +**Synonym.** A synonym is a word or phrase that means exactly or nearly the same as another word or phrase in a given language. This subcategory aims to test whether models can identify two semantically equivalent questions whose surface forms only differ in a pair of synonyms. As in example 16, the two sentences differ only in two words, both of which refer to Kiwifruit, so they have the same meaning. + +Antonym. In contrast to synonyms, antonyms are words within an inherently incompatible binary relationship. This subcategory examines model's capability on distinguishing words with opposed meanings. We mainly focus on adjective's opposite, e.g., "高high" and "低low" (see example 20). + +Negation. Negation is another way to express contradiction. To negate a verb or an adjective in Chinese, we normally put a negative before it, e.g., "不not" before "哭cry" (example 21), "不是not" before "红的red" (example 22). The negative before the verb or the adjective negates the statement. It is an effective way to analyze model's basic skill of figuring out the contradictory meanings even there is only a minor change. + +Moreover, we include some equivalent paraphrases with negation in this subcategory. In example 23, "无法平静can't calm down" is the negative paraphrase of "激动excited", so that the paraphrase sentence is equivalent to the positive sentence. We believe that a robust QM system should be able to recognize this kind of paraphrase question pairs. + +**Temporal Word.** Temporal reasoning is the relatively higher-level linguistic capability that allows the model to reason about a mathematical timeline. Unlike English, verbs in Chinese do not have morphological inflections. Tenses and aspects are expressed either by temporal noun phrases like " $\mathcal{F}$ tomorrow" (examples 24) or by aspect particles like " $\mathcal{F}$ le", which indicates the completion of an action (examples 25). This subcategory focuses on the temporal distinctions and helps us evaluate the models' temporal reasoning capability. + +While single word sense is important to question meaning, how words composed together into a whole also affects sentence understanding. We believe the relations among words in a sentence is important information for models to capture, so we focus on several types of syntactic features in this category. We pre-define 4 linguistic phenomena that we believe is meaningful to locate model's strength and weakness, and describe them here. + +Symmetry. Sometimes paraphrases can be generated by only swapping the two conjuncts around in a structure of coordination. As shown in example 26, "鱼fish" and "鸡蛋egg" are joined together by the conjunction "和and", which have the symmetric relation to each other. Even if we swap them around, the sentence meaning will not change. We name this subcategory Symmetry, with which we aim to explore if a model captures the inherent dependency relationship between words. + +Asymmetry. Some words (such as "和and") denote symmetric relations, while others (for example, preposition "到to") denote asymmetric. Example 27 shows a sentence pair in which the word before the preposition "到to" is an adverbial and the word after it is the object. Swapping around the adverbial and the object of the prepositional phrase will definitely leads to a nonequivalent meaning. If a model performs well only on subcategory Symmetry or Asymmetry, it may rely on shortcuts instead of the understanding of the syntactic information. Negative Asymmetry. To further explore the syntactic capability of QM model, DuQM includes a set of test examples which consider both syntactic asymmetry and antonym, and we name this category Negative Asymmetry. In example 28, the asymmetric relation between "男人men" and "女 人women" and the opposite meaning of "高taller" and "矮shorter" resolve to an equivalent meaning. With this subcategory, we can better explore model's capability of inferring more complex syntactic structure. + +**Voice.** Another crucial syntactic capability of models is to differentiate active and passive voices. In Chinese, the most common way to express the passive voice is using Bei-constructions which feature an agentive case marker "被bei". The subject of a Bei-construction is the patient of an action, and the object of the preposition "被bei" is the agent. Compared to Fig.1(a), the additional "被bei" and the change of word order of "猫cat" and "狗dog" + +![](_page_4_Figure_0.jpeg) + +What to do if dog is bitten by cat (c) Passive voice non-paraphrase question. + +Figure 1: The dependency relations of active voice and passive voice questions. + +in Fig[.1\(b\)](#page-4-3) convert the sentence from active to passive voice, but the two sentences have the same meaning. If we further change the word order from Fig[.1\(b\)](#page-4-3) to Fig[.1\(c\),](#page-4-4) the sentence still uses passive voice but has different meaning. + +Passive voice is not always expressed with an overt "被*bei*". Sometimes a sentence without any passive marker is still in passive voice. In example 29, although the first sentence is without "被*bei*", it expresses the same meaning as the second one. There are a set of active-passive examples in this category, which are effective to evaluate model's performance on active and passive voices. + +Lexical items ordered by syntactic rules are not all that make a sentence mean what it means. Context, or the communicative situation that influence language use, has a part to play. We include some pragmatic features in DuQM so as to observe whether models are able to understand the contextual meaning of sentences. + +Misspelling. Misspellings are quite often seen by search engines and question-answering systems, which are mostly unintentional. Models should have the capability to capture the true intention of the questions with spelling errors to ensure the robustness. In example 30, despite the misspelled word "文身*tatoo*" the two questions mean the same, In some real world situations, models should understand misspellings appropriately. For example, + +![](_page_4_Figure_8.jpeg) + +Figure 2: Construction process of DuQM. + +when users search a query but type in misspelling, a robust model will still give the correct result. + +Discourse Particle. Discourse particles are words and small expressions that contribute little to the information the sentence convey, but play some pragmatic functions such as showing politeness, drawing attention, smoothing utterance, etc. As in example 32, the word "求助*help*" is used to draw attention and bring no additional information to the sentence. Whether using these little words do not change the sentence meaning. It is necessary to a model to identify the semantic equivalency when such words are used. + +We design DuQM as a *diverse* and *natural* corpus. The construction process of DuQM is divided into 4 steps and illustrated in Fig. [2.](#page-4-5) Firstly, we preprocess the source questions to obtain their linguistic knowledge, which will be used to perturb the source texts. Then we pair the source and perturbed question as an example. The examples' naturalness is reviewed by human evaluators. At last, the examples are annotated manually and DuQM is finally constructed. We introduce the construction details in the following: + +Linguistic Preprocessing. We collect a large number of source questions from the search query log of a commercial search engine. All the source questions are natural and then we perform several linguistic preprocessings on them: named entity recognition, POS tagging, dependency parsing, and word importance analysis. The linguistic knowledge about the source questions we obtained in this step will be used for perturbation. + +Perturbation. We conduct different perturbation + +operations for different subcategories. In general, we perturb the sentences in 3 ways: + +- replace: replace a word with another word, e.g., for category *Synonym*, we replace one word with its synonym; +- insert : insert an additional word, e.g., for category *Temporal word*, we insert temporal word to the source question; +- swap: swap two words. This operation is only used in *Syntactic Feature*. + +The perturbations of all categories are listed in column *Perturbation Operation* of Tab. [1,](#page-2-0) and the perturbation details will be given in Appendix [A.](#page-10-0) Naturalness Review. To ensure the generated sentences are natural, we examine their appearances in the search log and only retain the sentences which have been entered into the search engine. + +Annotation. The source question and generated question are paired together as an example. Then the examples are evaluated by evaluators from our internal data team. They need to evaluate whether the examples are fluent, grammatically correct, and correctly categorized. The low-quality examples are discarded and the examples with inappropriate categories are re-classified. + +Then the question pairs are annotated by the linguistic experts from our internal data team. Semantically equivalent question pairs are positive examples, and inequivalent pairs are negative. The annotators are required a approval rate higher than 99% for at least 1,000 prior tasks. Each example is annotated by three annotators, and the examples will be tagged with the label which more than 2 annotators choose. To further ensure the annotation quality, 10% of the annotated examples are selected randomly and reviewed by another senior linguistic expert and and if the review accuracy is lower than 95%, the annotation linguistic experts need to re-annotate all the examples until the accuracy is higher than 95%. Since all annotators are linguistic experts from our internal data team instead of crowd-sourcing, we do not need to use IAA to measure the annotation quality. Generally, only 0.002% (20/10,167) generated examples are not fluent or not grammatically correct, and only 0.02% (195/10,121) generated examples are re-annotated manually. The overall annotation process is illustrated in Fig. [3.](#page-11-0) + +Eventually, we generate 10,121 examples for DuQM. The class distribution of all categories are + + + +| | | Length | # | | | | +|-------------|------|--------|-------|-------|--------|--| +| Category | q | q' | Y | N | All | | +| Lexical | 8.58 | 8.89 | 1,315 | 6,784 | 8,099 | | +| Syntactic | 9.86 | 9.89 | 678 | 532 | 1,210 | | +| Pragmatic | 8.73 | 9.03 | 812 | 0 | 812 | | +| Avg / Total | 8.74 | 8.90 | 2,805 | 7,316 | 1,0121 | | + +Table 2: Data statistics of DuQM. + + + +| Model | LCQMCtest | DuQM | 4 | +|----------|-----------|----------|-------| +| BERTb | 87.1±0.1 | 66.6±0.6 | -20.5 | +| ERNIEb | 87.3±0.1 | 69.8±0.3 | -17.5 | +| RoBERTab | 87.2±0.4 | 69.5±0.1 | -17.7 | +| MacBERTb | 87.4±0.3 | 70.3±0.6 | -17.1 | +| RoBERTal | 87.7±0.1 | 73.8±0.3 | -13.9 | +| MacBERTl | 87.6±0.1 | 73.8±0.5 | -13.8 | + +Table 3: Accuracy(%) on LCQMCtest and DuQM. b indicates base, and l indicates large. + +given in Tab. [1.](#page-2-0) Additional data statistics are provided in Tab. [2.](#page-5-1) diff --git a/2201.00520/main_diagram/main_diagram.drawio b/2201.00520/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e75d2c7de0f167e951d3adafc7ca88a526a59554 --- /dev/null +++ b/2201.00520/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2201.00520/paper_text/intro_method.md b/2201.00520/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..4cf9675dd96435e2ac011a2f749d27c84744af93 --- /dev/null +++ b/2201.00520/paper_text/intro_method.md @@ -0,0 +1,117 @@ +# Introduction + +Transformer [\[34\]](#page-11-0) is originally introduced to solve natural language processing tasks. It has recently shown great potential in the field of computer vision [\[12,](#page-10-0)[26,](#page-11-1)[36\]](#page-11-2). The pioneer work, Vision Transformer [\[12\]](#page-10-0) (ViT), stacks multiple Transformer blocks to process non-overlapping image patch (*i.e.* visual token) sequences, leading to a convolutionfree model for image classification. Compared to their CNN counterparts [\[18,](#page-10-1)[19\]](#page-10-2), Transformer-based models have larger receptive fields and excel at modeling long-range dependencies, which are proved to achieve superior performance in the regime of a large amount of training data and + +![](_page_0_Figure_9.jpeg) + +Figure 1. Comparison of DAT with other Vision Transformer models and DCN in CNN model. The red star and the blue star denote the different queries, and masks with solid line boundaries denote the regions to which the queries attend. In a data-agnostic way: (a) ViT [\[12\]](#page-10-0) adopts full attention for all queries. (b) Swin Transformer [\[26\]](#page-11-1) uses partitioned window attention. In a datadependent way: (c) DCN [\[9\]](#page-10-3) learns different deformed points for each query. (d) DAT learns shared deformed points for all queries. + +model parameters. However, the superfluous attention in visual recognition is a double-edged sword, and has multiple drawbacks. Specifically, the excessive number of keys to attend per query patch yields high computational cost and slow convergence, and increases the risk of overfitting. + +In order to avoid excessive attention computation, existing works [\[6,](#page-10-4) [11,](#page-10-5) [26,](#page-11-1) [36,](#page-11-2) [43,](#page-11-3) [49\]](#page-11-4) have leveraged carefully designed efficient attention patterns to reduce the computation complexity. As two representative approaches among them, Swin Transformer [\[26\]](#page-11-1) adopts window-based local + +\*Equal contribution. + +Corresponding author. + +attention to restrict attention in local windows, while Pyramid Vision Transformer (PVT) [\[36\]](#page-11-2) downsamples the key and value feature maps to save computation. Though effective, the hand-crafted attention patterns are data-agnostic and may not be optimal. It is likely that relevant keys/values are dropped, while less important ones are still kept. + +Ideally, one would expect that the candidate key/value set for a given query is flexible and has the ability to adapt to each individual input, such that the issues in hand-crafted sparse attention patterns can be alleviated. In fact, in the literature of CNNs, learning a deformable receptive field for the convolution filters has been shown effective in selectively attending to more informative regions on a datadependent basis [\[9\]](#page-10-3). The most notable work, Deformable Convolution Networks [\[9\]](#page-10-3), has yielded impressive results on many challenging vision tasks. This motivates us to explore a deformable attention pattern in Vision Transformers. However, a naive implementation of this idea leads to an unreasonably high memory/computation complexity: the overhead introduced by the deformable offsets is quadratic *w.r.t* the number of patches. As a consequence, although some recent work [\[7,](#page-10-6) [46,](#page-11-5) [54\]](#page-11-6) have investigated the idea of deformable mechanism in Transformers , none of them have treated it as a basic building block for constructing a powerful backbone network like the DCN, due to the high computational cost. Instead, their deformable mechanism is either adopted in the detection head [\[54\]](#page-11-6), or used as a preprocessing layer to sample patches for the subsequent backbone network [\[7\]](#page-10-6). + +In this paper, we present a simple and efficient deformable self-attention module, equipped with which a powerful pyramid backbone, named *Deformable Attention Transformer* (DAT), is constructed for image classification and various dense prediction tasks. Different from DCN that learns different offsets for different pixels in the whole feature map, we propose to learn a few groups of query agnostic offsets to shift keys and values to important regions (as illustrated in Figure [1\(](#page-0-0)d)), based on the observation in [\[3,](#page-9-0) [52\]](#page-11-7) that global attention usually results in the almost same attention patterns for different queries. This design both holds a linear space complexity and introduces a deformable attention pattern to Transformer backbones. Specifically, for each attention module, reference points are first generated as uniform grids, which are the same across the input data. Then, an offset network takes as input the query features and generates the corresponding offsets for all the reference points. In this way, the candidate keys/values are shifted towards important regions, thus augmenting the original self-attention module with higher flexibility and efficiency to capture more informative features. + +To summarize, our contributions are as follows: we propose the first deformable self-attention backbone for visual recognition, where the data-dependent attention pattern endows higher flexibility and efficiency. Extensive experiments on ImageNet [\[10\]](#page-10-7), ADE20K [\[51\]](#page-11-8) and COCO [\[25\]](#page-11-9) demonstrate that our model outperforms competitive baselines including Swin Transformer consistently, by a margin of 0.7 on the top-1 accuracy of image classification, 1.2 on the mIoU of semantic segmentation, 1.1 on object detection for both box AP and mask AP. The advantages on small and large objects are more distinct with a margin of 2.1. + +# Method + +We first revisit the attention mechanism in recent Vision Transformers. Taking a flattened feature map x ∈ R N×C as the input, a multi-head self-attention (MHSA) block with M heads is formulated as + +$$q = xW_q, \ k = xW_k, \ v = xW_v, \tag{1}$$ + +$$z^{(m)} = \sigma(q^{(m)}k^{(m)\top}/\sqrt{d})v^{(m)}, m = 1, \dots, M,$$ + (2) + +$$z = \operatorname{Concat}\left(z^{(1)}, \dots, z^{(M)}\right) W_o, \tag{3}$$ + +where σ(·) denotes the softmax function, and d = C/M is the dimension of each head. z (m) denotes the embedding output from the m-th attention head, q (m) , k(m) , v(m) ∈ R N×d denote query, key, and value embeddings respectively. Wq, Wk, Wv, Wo ∈ R C×C are the projection matrices. To build up a Transformer block, an MLP block with two linear transformations and a GELU activation is usually adopted to provide nonlinearity. + +With normalization layers and identity shortcuts, the l-th Transformer block is formulated as + +$$z'_{l} = MHSA(LN(z_{l-1})) + z_{l-1},$$ + (4) + +$$z_l = \text{MLP}\left(\text{LN}(z_l')\right) + z_l',\tag{5}$$ + +where LN is Layer Normalization [\[1\]](#page-9-2). + +Existing hierarchical Vision Transformers, notably PVT [\[36\]](#page-11-2) and Swin Transformer [\[26\]](#page-11-1) try to address the challenge of excessive attention. The downsampling technique of the former results in severe information loss, and the shiftwindow attention of the latter leads to a much slower growth of receptive fields, which limits the potential of modeling large objects. Thus a data-dependent sparse attention is required to flexibly model relevant features, leading to deformable mechanism firstly proposed in DCN [\[9\]](#page-10-3). However, simply implementing DCN in Transformer models is a non-trivial problem. In DCN, each element on the feature map learns its offsets individually, of which a 3 × 3 deformable convolution on an H ×W ×C feature map has the space complexity of 9HW C. If we directly apply the same mechanism in the attention module, the space complexity will drastically rise to NqNkC, where Nq, Nk are the number of queries and keys and usually have the same scale as the feature map size HW, bringing approximately a biquadratic complexity. Although Deformable DETR [\[54\]](#page-11-6) has managed to reduce this overhead by setting a lower number of keys with Nk = 4 at each scale and works well as a detection head, it is inferior to attend to such few keys in a backbone network because of the unacceptable loss of information (see detailed comparison in Appendix). In the meantime, the observations in [\[3,](#page-9-0)[52\]](#page-11-7) have revealed that different queries have similar attention maps in visual attention models. Therefore, we opt for a simpler solution with shared shifted keys and values for each query to achieve an efficient trade-off. + +Specifically, we propose deformable attention to model the relations among tokens effectively under the guidance of the important regions in the feature maps. These focused regions are determined by multiple groups of deformed sampling points which are learned from the queries by an offset network. We adopt bilinear interpolation to sample features from the feature maps, and then the sampled features are fed to the key and value projections to get the deformed keys and values. Finally, standard multi-head attention is applied to attend queries to the sampled keys and aggregate features from the deformed values. Additionally, the locations of deformed points provide a more powerful relative position bias to facilitate the learning of the deformable attention, which will be discussed in the following sections. + +**Deformable attention module.** As illustrated in Figure **2**(a), given the input feature map $x \in \mathbb{R}^{H \times W \times C}$ , a uniform grid of points $p \in \mathbb{R}^{H_G \times W_G \times 2}$ are generated as the references. Specifically, the grid size is downsampled from the input feature map size by a factor r, $H_G = H/r$ , $W_G =$ W/r. The values of reference points are linearly spaced 2D coordinates $\{(0,0),\ldots,(H_G-1,W_G-1)\}$ , and then we normalize them to the range [-1, +1] according to the grid shape $H_G \times W_G$ , in which (-1, -1) indicates the top-left corner and (+1,+1) indicates the bottom-right corner. To obtain the offset for each reference point, the feature maps are projected linearly to the query tokens $q = xW_q$ , and then fed into a light weight sub-network $\theta_{\text{offset}}(\cdot)$ to generate the offsets $\Delta p = \theta_{\text{offset}}(q)$ . To stabilize the training process, we scale the amplitude of $\Delta p$ by some predefined factor s to prevent too large offset, *i.e.*, $\Delta p \leftarrow s \tanh{(\Delta p)}$ . Then the features are sampled at the locations of deformed points as keys and values, followed by projection matrices: + +$$q = xW_q, \ \tilde{k} = \tilde{x}W_k, \ \tilde{v} = \tilde{x}W_v, \tag{6}$$ + +with +$$\Delta p = \theta_{\text{offset}}(q), \ \tilde{x} = \phi(x; p + \Delta p).$$ + (7) + + $\tilde{k}$ and $\tilde{v}$ represent the deformed key and value embeddings respectively. Specifically, we set the sampling function $\phi(\cdot;\cdot)$ to a bilinear interpolation to make it differentiable: + + +$$\phi\left(z;(p_{x},p_{y})\right) = \sum_{(r_{x},r_{y})} g(p_{x},r_{x})g(p_{y},r_{y})z[r_{y},r_{x},:], \quad (8)$$ + +where $g(a,b) = \max(0,1-|a-b|)$ and $(r_x,r_y)$ indexes all the locations on $z \in \mathbb{R}^{H \times W \times C}$ . As g would be non-zero only on the 4 integral points closest to $(p_x,p_y)$ , it simplifies Eq.(8) to a weighted average on 4 locations. Similar to existing approaches, we perform multi-head attention on q,k,v and adopt relative position offsets R. The output of an attention head is formulated as: + +$$z^{(m)} = \sigma \left( q^{(m)} \tilde{k}^{(m)\top} / \sqrt{d} + \phi(\hat{B}; R) \right) \tilde{v}^{(m)}, \quad (9)$$ + +where $\phi(\hat{B}; R) \in \mathbb{R}^{HW \times H_G W_G}$ correspond to the position embedding following previous work [26] while with several adaptations. Details will be explained later in this section. Features of each head are concatenated together and projected through $W_o$ to get the final output z as Eq.(3). + +**Offset generation.** As we have stated, a sub-network is adopted for offset generation which consumes the query + +features and outputs the offset values for reference points respectively. Considering that each reference point covers a local $s \times s$ region (s is the largest value for offset), the generation network should also have the perception of the local features to learn reasonable offsets. Therefore, we implement the sub-network as two convolution modules with a nonlinear activation, as depicted in Figure 2(b). The input features are first passed through a $5\times 5$ depthwise convolution to capture local features. Then, GELU activation and a $1\times 1$ convolution is adopted to get the 2D offsets. It is also worth noticing that the bias in $1\times 1$ convolution is dropped to alleviate the compulsive shift for all locations. + +**Offset groups.** To promote the diversity of the deformed points, we follow a similar paradigm in MHSA, and split the feature channel into G groups. Features from each group use the shared sub-network to generate the corresponding offsets respectively. In practice, the head number M for the attention module is set to be multiple times of the size of offset groups G, ensuring that multiple attention heads are assigned to one group of deformed keys and values. + +**Deformable relative position bias.** Relative position bias encodes the relative positions between every pair of query and key, which augments the vanilla attention with spatial information. Considering a feature map with shape $H\times W$ , its relative coordinate displacements lie in the range of [-H,H] and [-W,W] at two dimensions respectively. In Swin Transformer [26], a relative position bias table $\hat{B} \in \mathbb{R}^{(2H-1)\times(2W-1)}$ is constructed to obtain the relative position bias B by indexing the table with the relative displacements in two directions. Since our deformable attention has continuous positions of keys, we compute the relative displacements in the normalized range [-1,+1], and then interpolate $\phi(\hat{B};R)$ in the parameterized bias table $\hat{B} \in \mathbb{R}^{(2H-1)\times(2W-1)}$ by the continuous relative displacements in order to cover all possible offset values. + +Computational complexity. Deformable multi-head attention (DMHA) has a similar computation cost as the counterpart in PVT or Swin Transformer. The only additional overhead comes from the sub-network that is used to generate offsets. The complexity of the whole module can be summarized as: + +$$\Omega(\text{DMHA}) = \underbrace{2HWN_{\text{S}}C + 2HWC^2 + 2N_{\text{S}}C^2}_{\text{vanilla self-attention module}} + \underbrace{(k^2 + 2)N_{\text{S}}C}_{\text{offset network}},$$ +(10) + +where $N_s = H_G W_G = HW/r^2$ is the number of sampled points. It can be immediately seen that the computational cost of the offset network has linear complexity w.r.t. the channel size, which is comparably minor to the cost for attention computation. Typically, consider the third stage of a Swin-T [26] model for image classification where H = W = 14, $N_s = 49$ , C = 384, the computational cost for the attention module in a single block is 79.63M FLOPs. If equipped with our deformable module (with k = 5), the ad- + +![](_page_4_Figure_0.jpeg) + +Figure 3. An illustration of DAT architecture. $N_1$ to $N_4$ are the numbers of stacked successive local attention and shift-window / deformable attention blocks. k and k denote the kernel size and stride of the convolution layer in patch embeddings. + +ditional overhead is 5.08M Flops, which is only 6.0% of the whole module. Additionally, by choosing a large downsample factor r, the complexity will be further reduced, which makes it friendly to the tasks with much higher resolution inputs such as object detection and instance segmentation. + +We replace the vanilla MHSA with our deformable attention in the Transformer (Eq.(4)), and combine it with an MLP (Eq.(5)) to build a deformable vision transformer block. In terms of the network architecture, our model, Deformable Attention Transformer, shares a similar pyramid structure with [7, 26, 31, 36], which is broadly applicable to various vision tasks requiring multiscale feature maps. As illustrated in Figure 3, an input image with shape $H \times W \times 3$ is firstly embedded by a 4×4 non-overlapped convolution with stride 4, followed by a normalization layer to get the $\frac{H}{4} \times \frac{W}{4} \times C$ patch embeddings. Aiming to build a hierarchical feature pyramid, the backbone includes 4 stages with a progressively increasing stride. Between two consecutive stages, there is a non-overlapped 2×2 convolution with stride 2 to downsample the feature map to halve the spatial size and double the feature dimensions. In classification task, we first normalize the feature maps output from the last stage and then adopt a linear classifier with pooled features to predict the logits. In object detection, instance segmentation and semantic segmentation tasks, DAT plays the role of a backbone in an integrated vision model to extract multiscale features. We add a normalization layer to the features from each stage before feeding them into the following modules such as FPN [23] in object detection or decoders in semantic segmentation. + +We introduce successive local attention and deformable attention blocks in the third and the fourth stage of DAT. The feature maps are firstly processed by a window-based local attention to aggregate information locally, and then passed through the deformable attention block to model the global relations between the local augmented tokens. This alternate design of attention blocks with local and global receptive fields helps the model learn strong representations, sharing a similar pattern in GLiT [5], TNT [15] and Point- + + + +| DAT Architectures | | | | | | | | +|--------------------------|--------------------|--------------------|---------------------|--|--|--|--| +| | DAT-T | DAT-S | DAT-B | | | | | +| Store 1 | $N_1 = 1, C = 96$ | $N_1 = 1, C = 96$ | $N_1 = 1, C = 128$ | | | | | +| Stage 1 $(56 \times 56)$ | window size: 7 | window size: 7 | window size: 7 | | | | | +| (30 × 30) | heads: 3 | heads: 3 | heads: 4 | | | | | +| Store 2 | $N_2 = 1, C = 192$ | $N_2 = 1, C = 192$ | $N_2 = 1, C = 256$ | | | | | +| Stage 2 $(28 \times 28)$ | window size: 7 | window size: 7 | window size: 7 | | | | | +| (20 × 20) | heads: 6 | heads: 6 | heads: 8 | | | | | +| | $N_3 = 3, C = 384$ | $N_3 = 9, C = 384$ | $N_3 = 9, C = 512$ | | | | | +| Stage 3 | window size: 7 | window size: 7 | window size: 7 | | | | | +| $(14 \times 14)$ | heads: 12 | heads: 12 | heads: 16 | | | | | +| | groups: 3 | groups: 3 | groups: 4 | | | | | +| | $N_4 = 1, C = 768$ | $N_4 = 1, C = 768$ | $N_4 = 1, C = 1024$ | | | | | +| Stage 4 | window size: 7 | window size: 7 | window size: 7 | | | | | +| $(7 \times 7)$ | heads: 24 | heads: 24 | heads: 32 | | | | | +| | groups: 6 | groups: 6 | groups: 8 | | | | | + +Table 1. Model architecture specifications. $N_i$ : Number of block at stage i. C: Channel dimension. window size: Region size in local attention module. heads: Number of heads in DMHA. groups: Offset groups in DMHA. + +former [29]. Since the first two stages mainly learn local features, deformable attention in these early stages is less preferred. In addition, the keys and values in the first two stages have a rather large spatial size, which greatly increase the computational overhead in the dot products and bilinear interpolations in deformable attention. Therefore, to achieve a trade-off between model capacity and computational burden, we only place deformable attention in the third and the fourth stage and adopt the shift-window attention in Swin Transformer [26] to have a better representation in the early stages. We build three variants of DAT in different parameters and FLOPs for a fair comparison with other Vision Transformer models. We change the model size by stacking more blocks in the third stage and increasing the hidden dimensions. The detailed architectures are reported in Table 1. Note that there are other design choices for the first two stages of DAT, e.g., the SRA module in PVT. We show the comparison results in Table 7. diff --git a/2202.12162/main_diagram/main_diagram.drawio b/2202.12162/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..564925af970c12118a7a8748858e8eb048a732dc --- /dev/null +++ b/2202.12162/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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5Zldd5owGMc/jZf1EB4IcFvXrjtnb63d2rsdJEFYkbAYp/bTL2hA0FjtBsV2eCH55wX4/Z+8QQ8Gk8V77mfRJ0Zo0jMNsujBu55pIgNc+Zcry7WCLW8tjHlMVKGNMIwfaVFTqbOY0GmtoGAsEXFWFwOWpjQQNc3nnM3rxUKW1K+a+WO6IwwDP9lV72IiorXq2sZGv6LxOCqujAyVM/GLwkqYRj5h84oEFz0YcMbE+myyGNAkh1dwuflwPbW+LG/4j6trb7Z8fLj/OT5bN3b5nCrlI3Cair9u+vsUvs0/D9N7/PFy8uvuNrpNQVUxfvvJTPFSzyqWBUDOZimheSOoB+fzKBZ0mPlBnjuXISO1SEwSlc2Z8EXMUpk0ZDKMk2TAEsZXTQFFxKaO1KeCswd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\ No newline at end of file diff --git a/2202.12162/paper_text/intro_method.md b/2202.12162/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..82f0be29421906786926daf845920a7e090a3b44 --- /dev/null +++ b/2202.12162/paper_text/intro_method.md @@ -0,0 +1,31 @@ +# Introduction + +Are our artificial intelligence systems capable of reasoning? Or like *Clever Hans*, they use various cues only tangentially related to the task and rely on rote memorization with poor generalization? [@pfungst1911clever; @Johnson_2017_CVPR] This work revisits such a question and proposes an interactive framework with the communication channel between two players. The first player, which reasoning capabilities we are about to test, performs visual reasoning tasks, we call it . The second player, which we call the , is manipulating the scene so that it *fools* the first player even though those changes still lead to correct reasoning steps among humans. Both players interact with each other only through questions, answers and the visual scene as shown in Figure [1](#two_agents){reference-type="ref" reference="two_agents"}. If the manipulating the scene causes the to change its answer even though the new scene is still valid for the same question and answer, it is then the reasoning failure. It is similar to the following situation. Imagine a box is placed between two spheres. If you ask a question, *is there a box between two spheres?*, the answer should be positive. Now, if we move the box anywhere so it does not cross any of the spheres, and ask the same question, the response should remain unchanged. In other words, we postulate that reasoning outputs of agents need to be invariant under scene configurations that are consistent with the questions-answer pairs. Moreover, in the spirit of generic adversarial attacks, we seek configurations that also pose little if any reasoning challenges for humans. + +We propose an automatic and agnostic pipeline to benchmark the reasoning capabilities of various models, only assuming they can communicate by answering questions about the scene. Due to the recent stream of research in vision-and-language [@VinVL; @Defense; @GraphReasoningVQA; @VLBert; @vcrcnn; @kamath2021mdetr; @lxmert; @Uniter], we believe there will be an increasing number of vision models that operate through language. Moreover, we also consider the visual question answering framework set-up as a two-player system as an excellent benchmarking pipeline. We perform all tests by scene manipulations and observing how a tested model behaves under such changes. The pipeline does not require any knowledge of the internals of the tested model. It also does not manipulate the sensory information of such a model, e.g., pixels in the images, and all the manipulations are physically meaningful. Even though our current pipeline uses synthetic scenes as only those can easily be automatically manipulated, our results have also real-world ramifications. If models are susceptible to semantically *meaningless* changes[^1] in scene configurations, in a synthetic setting, there are valid concerns that real-world robots could also be prone to manipulation of objects in a room. Finally, our work also questions the possibility of training and benchmarking networks in a purely data-driven and offline, static manner. + +The main contributions of our work could be summarized in three points. + +***First***, we propose a strong *black-box* adversarial test, which makes no assumptions about the underlying mechanics of a tested model, formulated as a game between two players. Our test does not require any direct access to the tested model, even through its sensory information. In particular, it does not require gradients, output probabilities, or any access to the perceived image. Our work also deviates from bounded perturbations and instead focuses on global scene manipulations that are still consistent with the task constraints, and can change the behavior of a tested model.\ +***Second***, we reformulate visual reasoning by integrating visual question answering with zero-sum two-player game frameworks. Under our novel formulation, a visual and adversary agents compete against each other through content manipulation. We believe that this is an initial step towards more sophisticated frameworks that integrate computer vision with multi-agent systems.\ +***Third***, we explore the limits of the data-driven approaches in synthetic visual scenarios, and demonstrate that current CLEVR models are lacking the efficiency to learn robust reasoning steps. + +# Method + +In this section, we explain briefly how the CLEVR dataset [@Johnson_2017_CVPR] is constructed and introduce our notation and definitions. is a synthetic visual question answering dataset introduced by @Johnson_2017_CVPR, which consists of about 700k training and 150k validation image-question-answer triplets. Images are artificially constructed and rendered from scene graphs -- a special structure containing information about object attributes such as position or color. Such a scene graph is also used to synthesize the ground-truth question-answer pairs by expanding templates according to the depth-first-search ordering. Ambiguous scenes are rejected. Each image represents an isometric view of the scene containing from two to ten objects. There are three classes of objects, *spheres*, *cubes* and *cylinders*. Each object can also be either large or small and has one color out of four (brown, purple, cyan, yellow). It can also be either metallic or rubber-made. Every object has $x$ and $y$ coordinates that are confined within the $(-3, +3)$ range. We use the same generation process to render modified scenes. Various models have been introduced to work with the CLEVR dataset, some even 'solving' the dataset by achieving near perfect performance. Despite the strong offline performance, we test if those models' performance perpetuates in the more interactive setting where configurations of the scene could be changed. Whenever possible, we use pre-trained CLEVR models. Otherwise, we train the remaining models from scratch by making sure we achieve results similar to published accuracy numbers on the validation set. We summarize all the models in Table [\[model_table\]](#model_table){reference-type="ref" reference="model_table"}. We show the accuracy on the CLEVR dataset (*Accuracy*), indicate if an architecture is trained from scratch (*Re-trained*), briefly describe how multi-modal fusion and reasoning is conducted (*Reasoning Mechanism*), and indicate any extra privileged information required during the training process (*Extra*). For instance, some models require extra access to functional programs used during the dataset generation, use scene graphs as a supervisory signal (states), or always operate on scene graphs (input-states). Otherwise, the models were trained only from image-question-answer triples. We formulate our problem as a between two players, and . The takes as input question-image pairs and provide answers to such questions. Some models use states (scene-graphs) that replace images or require programs [@Johnson_2017_CVPR]. The whole game consists of all CLEVR data points. For our purpose, we extend the notion of the into . The rules of are identical to the whole . The only difference is that each operates on a subset of the CLEVR dataset. We define the size of a by the number of datapoints that are attached to that . We sample data points for each randomly and mutually exclusively. have analogies in the adversarial perturbations literature. of size one resemble per-image adversarial perturbations [@FGSM; @Deepfool] whereas a that has all data points is similar to universal adversarial perturbations [@moosavi2017universal]. In this work, we investigate various sizes but due to the sheer scale we were unable to use the whole game as the . Larger make the optimization process more difficult as the domain where the needs to operate increases. The training is also much more time-consuming and a sequential process. Instead, we can train multiple players on different independently and thus massively. We leave the arduous training of the universal on the whole as a possible future direction. + +::: center +::: + +We need to ensure that create valid scenes that are *consistent* and *in-distribution*. Both properties are guaranteed by our environment enforcers. Since scene manipulation may change the answer for a given question, we need to ensure this does not happen. That is, the new scene is still *consistent* with the question-answer pair. The question-relevance enforcer achieves that by running functional programs associated with each question [@Johnson_2017_CVPR] on the modified scene-graph. Hence, it gets the new ground-truth answer. The enforcer rejects the new scene if that new answer differs from the previous one. In this way, it guarantees the newly generated scenes give the same answers as the original scenes on the same question. Thus, we can generate equivalent scenes containing the same objects that have identical answers for the same questions. Using that enforcer, we can test if the 's answers are invariant under such an equivalent class of scenes. Even with the question-relevance enforcer, the may still produce undesired outputs. For instance, it can stretch the whole scene thus violating the scene boundaries from the original CLEVR dataset, making, e.g., everything to look very small ( in the appendix). Although it is still an interesting form of adversarial scene manipulation, we focus rather on the *in-distribution* scene manipulations that respect the original boundaries. To enforce that property, we use a scene-constraint enforcer that checks the boundaries of the scene. Without that enforcer, the would quickly resort to stretching the whole scenes, achieving a form of adversarial attack that uses distribution shifts rather than content manipulation. It does so, e.g., by moving the camera away until objects are barely visible. We give a few such examples in the appendix (). + +
+
+ +
+
Our game between two players: and . uses a multi-modal module to extract features conditioned on the visual and textual inputs. After transforming such features with a feed-forward architecture, it samples an action using object-specific heads. Each action corresponds to manipulating the corresponding object in the scene. In the case of missing objects, we use an token. After alternating the original scene graph, we use various environment enforcers to ensure validity of the constructed scene. A valid scene graph is rendered and introduced to the together with the original image. Finally, we collect responses of the and calculate suitable rewards based on them, and we repeat the whole cycle during the training phase.
+
+ +Meaningful scene manipulations require not only generic scene understanding, but also the ability to distinguish which objects to displace and how. Hence, the player is a composition of a multi-modal module, which creates input representation, and a decision maker, which decides how to control the scene. Figure [1](#two_agents){reference-type="ref" reference="two_agents"} illustrates the and the game between both players. We have experimented with the same multi-modal modules as those in , but found out we have a better performance and the convergence rate if the operates on the scene-graphs (states) instead of pixels. For that, we use *state-input* variant of Relation Networks [@Santoro_2018_NEURIPS]. The model receives as the input $10 * 6$ object tokens, and question tokens. Every object token represents one-out-of-ten possible objects in the scene by its attributes such as position, color, shape, material and size. If the scene has fewer than ten objects, we use $\emptyset$ token to indicate that, which also acts as a padding. We also have special tokens that separate questions from the objects which we add as a latent embedding, e.g., $\text{emb}(\text{material}) + \text{emb}(\text{object})$. Such an input encoding is similar to our *State-Input Transformer* and described in . The embedded vectors are given to the Relation Network (RN). Finally, we train that network on the CLEVR visual question answering task, where we achieve $97.6\%$ on the validation set, and use the representation just after the last relational layer for the decision maker. Inspired by the work on reinforcement learning [@a2c; @conta2c; @ddpg; @sea2c; @trpo], we use an actor-critic module that acts on scenes. The actor is a general-purpose fully connected layer with ten object-specific heads. Each head is randomly assigned to a unique object in the scene for its manipulation. Every head produces a displacement in $x$ and $y$ coordinates of the corresponding object. Although we have initially experimented with the continuous output space, we have found out the following simple strategy is more effective. First, we discretize all the $x$ and $y$ coordinates into $N$ bins each. Now, each head produces two $N$-dimensional vectors that are next projected into a probabilistic space via softmax. Next, we sample displacements in $x$ and $y$ axis independently from both softmax distributions. Note that, even though we do not model the joint distribution explicitly due to computational reasons, both samples condition on the common head and thus are only *conditionally* independent of each other. We discretize the scene where each axis has values in $[-3,3]$ onto $N=7$ bins per axis. Our critic is a simple three layer feed-forward network (with relu as activations) that predicts a reward score between $-1$ and $+1$ via tanh activation ($1.2*\text{tanh}$ for better numerical properties). Due to our formulation of and the environment, we can benchmark various reasoning models purely in the *black-box* setting via a series of questions about the scene. manipulates the scene so that it is still consistent with the question-answer pair. The manipulations are applied to scene-graphs, and the resulting scene-graph is evaluated by the environment enforcers described in . Invalid scenes are thus discarded. In this way, we ensure the *in-distribution* and *consistency* in the scene generation. Original image-question pairs are fed to a that produces corresponding answers. We refer to that answers as . After the scene manipulation, new images paired with the same questions are also given to the that produces . We construct rewards based on , and *ground-truth answers*. + +If the forces the to change the answer, i.e., an is different than a , it gets *Consistency Drop Reward* (*cr*). If an is the *ground-truth* answer, it gets instead *Accuracy Drop Reward* (*dr*). Both rewards differentiate between simply confusing the model and causing a drop in its performance. If produces an invalid scene it gets *Invalid Scene Reward* (*isr*). This reward encourages producing scenes that pass the environment enforcers tests. Finally, if does not manage to fool the model, it gets *Fail Reward* (*fr*). We use the following values: *dr*$= 1$, *cr*$= 0.1$, *fr* $= -0.1$, *isr* $= -0.8$. To train we use the A2C algorithm with the episode length set to one as we do not need to model long-range consequences of the decision-making mechanism. Batches contain images, question, answers, programs and scene-graphs. We train the for each independently using the same architecture. We experiment with the following sizes $10, 100, 1000$. All are constructed randomly. Under our discretization scheme, the action space is $N^k$ where $N$ is the number of bins and $k$ is the number of objects in the scene. In practice, it is up to $49^{10}$. diff --git a/2208.02080/main_diagram/main_diagram.drawio b/2208.02080/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..36d70e868a7e96a9020061c5748dece416fd02f2 --- /dev/null +++ b/2208.02080/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2208.02080/main_diagram/main_diagram.pdf b/2208.02080/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d448474ab8455813a355178e0906a751fe7ba488 Binary files /dev/null and b/2208.02080/main_diagram/main_diagram.pdf differ diff --git a/2208.02080/paper_text/intro_method.md b/2208.02080/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..228ac2e49a435182f7e99261da59b679695ffe1b --- /dev/null +++ b/2208.02080/paper_text/intro_method.md @@ -0,0 +1,19 @@ +# Introduction + +The amount of user-generated video content uploaded to the Internet every minute is ever increasing, leading to more than 500 hours of content uploaded to YouTube every minute, as of February 2020 [\[6\]](#page-7-0). Finding the relevant videos for a given query requires a mix of computer vision and natural language processing techniques, placing this problem at the intersection of the two communities. In particular, the text-to-video retrieval task encompasses this objective by requiring to sort all the videos based on their semantic closeness to the input query. Another task, which is similar to textto-video retrieval and is used to holistically evaluate a method, is the video-to-text retrieval task, which switches the role of video and query. In general, with the term text-video retrieval both tasks are considered and, given its cross-modal nature, it involves both visual and textual understanding. + +MM '22, October 10–14, 2022, Lisboa, Portugal + +© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 30th ACM International Conference on Multimedia (MM '22), October 10–14, 2022, Lisboa, Portugal, [https://doi.org/10.1145/3503161.3548365.](https://doi.org/10.1145/3503161.3548365) + +Recently, deep learning techniques were used to automatically extract features from the multimodal data and learn how to solve this task, showing their potential and achieving impressive results [\[9,](#page-7-1) [45,](#page-8-0) [58\]](#page-8-1). However, a significant limitation in the success of these techniques is represented by the huge amount of annotated data required to perform the training of a deep learning model. To this end, large amounts of data were collected through crowdsourcing platforms where human efforts are required to carefully annotate the data, leading to tedious tasks for the annotators and huge costs for the dataset collectors. Examples of large scale datasets obtained with this approach include MSR-VTT [\[66\]](#page-9-1) and VATEX [\[57\]](#page-8-2). To reduce the costs of the collection, the scientific community mainly investigated two automatic solutions: web scraping and data augmentation. In the former, the extraction of visual content from the Internet and the related annotation is performed automatically, for instance with speech recognition [\[41\]](#page-8-3), alternative texts [\[3\]](#page-7-2), or by leveraging hashtags [\[19\]](#page-8-4). While this approach leads to possibly huge and rich datasets, the annotations are often noisy and it is difficult to guarantee the quality of the annotations. On the other hand, data augmentation techniques are often used to artificially increase the size of a dataset by leveraging the already available annotated samples: new samples can be obtained by applying labelpreserving techniques, hence providing semantically coherent data and avoiding the noise. Indeed, these techniques have shown a great potential in many fields, both from the vision community, such as classification [\[4,](#page-7-3) [28,](#page-8-5) [54,](#page-8-6) [67\]](#page-9-2) and detection [\[47,](#page-8-7) [71\]](#page-9-3), and the language processing community, such as text summarization [\[16,](#page-7-4) [44\]](#page-8-8) and text classification [\[29,](#page-8-9) [61\]](#page-8-10). Although augmentation was applied to visual question answering [\[50,](#page-8-11) [59\]](#page-8-12) and image captioning [\[10,](#page-7-5) [53\]](#page-8-13), these techniques are less explored for text-video retrieval. To address this shortcoming, we investigate the application of augmentation techniques and propose an augmentation technique for text-video retrieval which exploits multimodal information (visual and textual). In particular, our video augmentation strategy creates a new augmented video by mixing the visual features of two samples from the same class ('Video fusion' in Fig[.1\)](#page-0-0), therefore leveraging the high level concepts automatically extracted from the deeper layers of a CNN-based backbone. This is achieved by performing our augmentation in the feature space, as opposed to common transformations, such as the geometric and color space transformations used for images, which are applied on the raw data [\[28\]](#page-8-5). In fact, working in the feature space raises three additional advantages: the same technique can be applied to data coming from different modalities, for instance on both video and text as we show in this paper, without requiring considerable changes which, on the other hand, are likely required when trying to apply a technique defined on one modality (e.g., replacing a word with a synonym) on a completely different modality (e.g., on video); it does not rely on the availability of the original videos or frames, which are more difficult to share and are not always shareable due to privacy or copyright issues, e.g. more than 20% of the original videos of MSR-VTT were reported to be removed from YouTube [\[40\]](#page-8-14), whereas all the videos of MovieQA [\[52\]](#page-8-15) faced copyright issues; and finally it can be applied on pre-extracted features, making it overall less time- and resource-demanding. The augmented caption for the abovementioned video is also created by following the same principle ('Text fusion' in Fig[.1\)](#page-0-0), showing the general applicability + +of our technique to multiple types of media. Finally, to validate our approach, multiple experiments are performed on the recently released EPIC-Kitchens-100 dataset [\[11\]](#page-7-6). These experiments include: multiple ablation studies to demonstrate the effectiveness of our strategy and to motivate the design choices; several comparisons to augmentation techniques inspired from the literature; and finally, to give additional evidence of the usefulness of our method, we observe further improvements when our proposed technique is integrated with a state-of-the-art model. To support reproducibility, code and pretrained models are made publicly available on Github at https://github.com/aranciokov/FSMMDA\_VideoRetrieval. + +We organize the paper as follows. In Section [2](#page-1-0) we perform a literature review and contextualize our work into it. Then, in Section [3](#page-2-0) we described in detail the proposed technique. Several ablation studies and experiments are performed and discussed in Section [4,](#page-3-0) whereas in Section [5](#page-7-7) we conclude our paper. + +# Method + +As in the case of videos, we design the textual augmentation technique in the feature space. We define two criteria, $\psi_V(a,q)$ and $\psi_N(o,q)$ , to identify the captions which can become valid substitutes of a given q based on one of its actions a or entities o. For instance, $\psi_V(a,q)=\{d\mid a\in \mathsf{act}(d)\land \mathsf{ent}(q)\cap \mathsf{ent}(d)\neq\emptyset\}$ . + +Given these operators and a caption q, the augmentation is performed with chance $\chi$ , and the decision between actions and entities is taken with uniform chance ( $\chi$ is the same as in Section 3.1). After the selection of a valid candidate d (step 16), the latent representations of both q and d are extracted with a function g (steps 3 and 18) and then mixed with the function $\rho$ (step 19). As for the videos, we define $\rho$ as a mixing function working on the high level concepts extracted from the language model g, that is $\rho(\overline{q},\overline{d}) = \lambda \cdot \overline{q} + (1-\lambda) \cdot \overline{d}$ ('Text fusion' in Fig.1). diff --git a/2209.00638/main_diagram/main_diagram.drawio b/2209.00638/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e5cdb6cec21bc34d28b34820f1c5be3dbeee46f4 --- /dev/null +++ b/2209.00638/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2209.00638/main_diagram/main_diagram.pdf b/2209.00638/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..755adb074a628961cde80e8684593a84fb256da7 Binary files /dev/null and b/2209.00638/main_diagram/main_diagram.pdf differ diff --git a/2209.00638/paper_text/intro_method.md b/2209.00638/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..19a402284c984a1f8412aa578b46b1891f159762 --- /dev/null +++ b/2209.00638/paper_text/intro_method.md @@ -0,0 +1,91 @@ +# Introduction + +The ability to analyze, comprehend, and segment video content at a temporal level is crucial for many computer vision, video understanding, robotics, and surveillance applications. Recent state-of-the-art methods for action segmentation mainly formalize the task as a frame-wise classification problem; that is, the objective is to assign an action label to each frame, based upon the full sequence of video frames. We illustrate this general approach in Fig. 1 (a). However, this formulation suffers several drawbacks, such as over-segmentation when trained on relatively small datasets (which typically need to consist of expensive frame-level annotations). + +In this work, we propose an alternative approach to the action segmentation task. Our approach involves a transformer-based seq2seq architecture that aims to map from + +\* Equal contribution. + +![](_page_1_Figure_2.jpeg) + +Fig. 1. Using Transformers for Action Segmentation. Instead of frame-level predictions, which are prone to over-segmentation (a), we propose a seq2seq transformer model for segment-level predictions (b). To provide more direct feedback to the encoder we apply a frame-wise loss (c); the resulting features enhance the decoder predictions. However, duration prediction still suffers, so we focus on transcript prediction (d) and use a separate alignment decoder to fuse encoder and decoder features to arrive at an implicit form of duration prediction (e). + +the video frames directly to a *higher-level sequence* of action segments, *i.e.,* a sequence of action label / duration pairs that describes the full predicted segmentation. + +The basic structure of our model follows traditional Transformer-based seq2seq models: the encoder branch takes as input a sequence of video frames and maps them to a set of features with the same length; the decoder branch then takes these features as input and generates a predicted sequence of high-level action segments in an autoregressive manner. This approach, illustrated in Fig. 1 (b), is a natural fit for action segmentation because it allows the decoder to directly output sequences in the higherlevel description space. The main advantage over the frame-level prediction is that it is less prone to over-segmentation. + +However, this seemingly natural approach does not immediately perform well on the action segmentation task by itself. In contrast to language translation, action segmentation typically involves long input sequences of very similar frames opposed to short output sequences of action segments. This difference together with the relatively small amount of training videos, makes it challenging for the encoder and decoder to keep track of the full information flow that is necessary to predict the high-level segmentation alone. For this reason, we incorporate several modifications and additional loss terms into our system, which together make this approach compete with or improve upon the state-of-the-art. + +First, to provide more immediate feedback to the encoder, we employ a frame-wise loss that linearly classifies each frame with the corresponding action label given the encoder features, Fig. 1 (c). As a result, the encoder performs frame-wise classification with high localization performance, *i.e.,* high frame-wise accuracy, but low discrimination performance, *i.e.,* over-segmentation with low Edit distance to the ground truth. Nonetheless, its features provide the decoder an informative signal to predict the sequence of actions more accurately. This immediate auxiliary supervision signal allows the decoder to learn more discriminative features for different actions. While the framewise loss improves the transcript prediction, the decoder still suffers from low localization performance for duration prediction. As the next step, we fuse the decoder predictions with the encoder, for which we propose two solutions. First, we propose to fuse the discriminative features of the decoder with the encoder features via a cross-attention mechanism in an alignment decoder, Fig. 1 (d,e). Second, the high performance of our decoder on predicting transcripts and the high performance of our encoder on localizing actions allows us to effectively utilize the common post-processing algorithm such as FIFA [33] and Viterbi [30,21]. + +Finally, we further extend our proposed framework when only a weaker form of timestamp supervision is available. As mentioned before, the frame-wise prediction is vital in our Transformer model to cope with small datasets and long sequences of frames. In this case, when the frame-level annotations are not fully available, we assign a label to each frame by a constrained k-medoids clustering algorithm that takes advantage of timestamp supervision. Our simple proposed clustering method achieves a frame-wise accuracy of up to 81% on the training set, which can be effectively used to train our seq2seq model. We further show that the clustering method can also be used in combination with frame-wise prediction methods such as ASFormer [42]. + +We evaluate our model on three challenging action segmentation benchmarks: 50Salads [35], GTEA [12], and Breakfast [19]. While our method achieves competitive frame-wise accuracies compared to the state-of-the-art, our method substantially outperforms other approaches in predicting the action sequence of a video, which is measured by the Edit distance. By using Viterbi [30,21] or FIFA [33] as post-processing, our approach also achieves state-of-the-art results in terms of segmental F1 scores. To the best of our knowledge, this work is the first that utilizes Transformers in an autoregressive manner for action segmentation and is applicable to both the fully and timestamp supervised setup. + +# Method + +In this section, we introduce our Unified Video Action Segmentation model via Transformers (*UVAST*). The goal of action segmentation is to temporally segment long, untrimmed videos and classify each of the obtained segments. Current state-of-the-art methods are based on *frame-level* predictions – they assign an action label to each individual frame – which are prone to *over-segmentation*: The video is not accurately segmented into clean, continuous segments, but fragmented into many shorter pieces of alternating action classes. We challenge this view of frame-level predictions and propose a novel approach that directly predicts the segments. By focusing on *segment-level* predictions – an alternative but equivalent representation of segmentations – our method overcomes the deep-rooted over-segmentation problem of frame-level predictions. + +In this work, we view action segmentation from a sequence-to-sequence (seq2seq) perspective: mapping a sequence of video frames to a sequence of action segments, *e.g.*, as pairs of action label and segment duration. The Transformer model [38] has emerged as a particularly powerful tool for seq2seq tasks and may seem like the natural fit. The vanilla Transformer model consists of an encoder module that captures long-range dependencies within the input sequence and a decoder module that translates the input sequence to the desired output sequence in an auto-regressive manner. In contrast to language translation tasks, action segmentation faces a strong mismatch between input and output sequence lengths, *i.e.*, inputs are long and untrimmed videos with various sequence lengths, while outputs are relatively short sequences of action segments. Therefore, we incorporate several modifications to address these issues, which we will go over in more detail in the following. + +**Notation.** Given an input sequence of T frame-wise features $x_t$ , for frame $t \in \{1, \ldots, T\}$ , our goal is to temporally segment and classify the T frames. The ground-truth labels of a segmentation can be represented in two equivalent forms: 1) a sequence of frame-wise action labels $\hat{y}_t \in \mathcal{C}$ for frame t, where $\mathcal{C}$ is the set of action classes, 2) a sequence of segment-wise annotations, which consists of ground-truth segment action classes $\hat{a}_i \in \mathcal{C}$ (also known as transcript), and segment durations $\hat{u}_i \in \mathbb{R}_+$ for each segment $i \in \{1, \ldots, N\}$ . + +**Transformer Encoder.** Our input sequence $X \in \mathbb{R}^{T \times d}$ consists of T frame-wise features $x_t$ , where d denotes the feature dimension. We embed them using a linear layer and then feed them to the Transformer encoder, which consists of several layers and allows the model to capture long-range dependencies within the video via the self-attention mechanism. The output of the encoder, $E \in \mathbb{R}^{T \times d'}$ , is a sequence of frame-wise features $e_t$ , which will be used in the cross-attention module of the decoder. To provide direct feedback to the encoder, we apply a linear layer to obtain frame-level predictions for $e_t$ . This enables the encoder to accurately localize the action classes within the video and provides more informative features to the decoder. In practice, we use a modified version of the encoder proposed in [42], which locally restricts the self-attention mechanism and uses dilated convolutions (see supplemental material for more details). + +**Transformer Decoder.** Given a sequence of frame-wise features $E \in \mathbb{R}^{T \times d'}$ , we use a Transformer decoder to auto-regressively predict the transcript, *i.e.*, the action labels of the segments. Starting with a *start-of-sequence* (sos) token, we feed the sequence of segments $S \in \mathbb{R}^{N \times d'}$ – embedded using learnable class tokens and positional encoding – up until segment i to the decoder. Via the cross-attention between the current sequence of segments and frame-wise features, the decoder determines the next segment i+1 in the video. In principle, the decoder could predict the segment duration as well (Fig. 1 (c)), however, in practice we found that the decoder's duration prediction suffers from low localization performance, see Table 4. While it is sufficient to pick out a single or few frames in the cross-attention mechanism for predicting the correct action + +class of a segment, the duration prediction is more difficult since it requires to assign frames to a segment and count them. Since the number of segments is much smaller than the number of frames, the cross-attention mechanism tends to assign only a subset of the frames to the correct segment. To address this issue, we propose a separate decoder module, which fuses the discriminative decoder features with the highly localized encoder features to obtain a more accurate duration prediction, which we describe in Section 3.3. + +Although our ultimate goal is segment-level predictions, we provide feedback to both the encoder and decoder model to make the best use of the labels. To that end, we apply a frame-wise cross-entropy loss on the frame-level predictions of the encoder: + +$$\mathcal{L}_{\text{frame}} = -\frac{1}{T} \sum_{t=1}^{T} \log(y_{t,\hat{c}}), \tag{1}$$ + +where yt,c denotes the predicted probability of label c at time t, and cˆ denotes the ground-truth label of frame t. Analogously, we apply a segment-wise cross-entropy loss on the segment-level predictions of the decoder: + +$$\mathcal{L}_{\text{segment}} = -\frac{1}{N} \sum_{i=1}^{N} \log(a_{i,\hat{c}}), \tag{2}$$ + +where ai,c denotes the predicted probability of label c at segment i, and cˆ denotes the ground-truth label of segment i. + +Regularization via Grouping. To regularize the encoder and decoder predictions, we additionally apply *group-wise* cross-entropy losses. To that end, we group the frames and segments by ground-truth labels L = {c ∈ C|c ∈ {aˆ1, . . . , aˆn}} that occur in the video: Tc = {t ∈ {1, . . . , T}|yˆt = c} are the indices of frames with class c, and Nc = {i ∈ {1, . . . , N}|aˆi = c} the indices of segments with class c. We apply a cross-entropy loss to the averaged prediction of each group: + +$$\mathcal{L}_{\text{g-frame}} = -\frac{1}{|L|} \sum_{c \in L} \log \left( \frac{1}{|T_c|} \sum_{t \in T_c} y_{t,c} \right)$$ +(3) + +$$\mathcal{L}_{g\text{-segment}} = -\frac{1}{|L|} \sum_{c \in L} \log \left( \frac{1}{|N_c|} \sum_{i \in N_c} a_{i,c} \right)$$ + (4) + +We utilize a loss through a cross-attention mechanism between the encoder and decoder features to allow further interactions between them. Let us assume that T video frames and corresponding N actions in the encoder and decoder are represented by their features $E \in \mathbb{R}^{T \times d'}$ and $D \in \mathbb{R}^{N \times d'}$ , respectively. The cross-attention loss involves obtaining a cross-attention matrix $M = \mathtt{softmax}(\frac{ED^T}{\tau' \sqrt{d'}})$ , where $\tau'$ is a stability temperature, and each row of M includes a probability vector that assigns each encoder feature (frame) to decoder features (actions). We then use M in the following cross-entropy loss function: + +$$\mathcal{L}_{CA}(M) = -\frac{1}{T} \sum_{t} \log(M_{t,\hat{n}}), \tag{5}$$ + +where $\hat{n}$ is the ground-truth segment index to which frame t belongs. We use this loss in our transcript decoder (main decoder) and alignment decoder in the following. + +Cross-Attention Loss for the Transcript Decoder. The cross-attention loss, when applied to the transcript decoder, provides more intermediate feedback to the decoder about the action location in the input sequence, see Fig. 5. We found this loss function especially effective on smaller datasets such as 50Salads (see Table 5). Our main objective for the encoder and the transcript decoder is: + +$$\mathcal{L} = \mathcal{L}_{\text{frame}} + \mathcal{L}_{\text{segment}} + \mathcal{L}_{\text{g-frame}} + \mathcal{L}_{\text{g-segment}} + \mathcal{L}_{\text{CA}}(M), \tag{6}$$ + +Cross-Attention Loss for the Alignment Decoder. While the transcript decoder generates the sequence of actions in a video, it does not predict the duration of each action. Although it is possible to predict the duration as well, as illustrated in Fig. 1 (c), the transcript decoder still struggles to localize actions through direct duration prediction as shown in Table 4. One reason for this could be the high mismatch between input and output sequence length and the relatively small number of training videos. While picking up a single segment frame is sufficient to predict the action class, the duration prediction effectively requires counting the number of frames in the segment, resulting in a more challenging task. Therefore, we design an alternative alignment decoder for predicting segment durations implicitly. The full flow of the complete model is shown in Fig. 2. + +A high Edit score of our decoder indicates that it has already learned discriminative features of the actions. The motivation for our alignment decoder is to align the encoder features to the highly discriminative features of the decoder, which can be further used for the duration prediction (see Fig 1 (e)). In essence, our proposed alignment decoder is a one-to-many mapping from the decoder features to the encoder features. The alignment decoder takes the encoder and decoder features $E \in \mathbb{R}^{T \times d'}$ and $D \in \mathbb{R}^{N \times d'}$ with positional encoding as input and generates the aligned features $A \in \mathbb{R}^{T \times d'}$ . Since the alignment decoder aims to explore the dependencies between the encoder features and the decoder features, we employ a cross-attention mechanism in its architecture similar to the transcript decoder. To this end, we compute an assignment matrix $\overline{M} \in \mathbb{R}^{T \times N}$ via cross-attention between the alignment decoder features (A) and positional encoded features of the transcript decoder (D) by $\overline{M} = \text{softmax}(\frac{AD^T}{\tau})$ with a small value of $\tau$ . Note that with a small value of $\tau$ each row of $\overline{M}$ will be close to a one-hot-encoding indicating the segment index the frame is assigned to. The positional encoding for D resolves ambiguities if the same action occurs at several locations in the video. + +In contrast to the decoder from the previous section, the alignment decoder is not auto-regressive since the full sequences of frame-wise and segment-wise features are + +![](_page_7_Figure_2.jpeg) + +Fig. 2. Overview of our complete model. Our complete model consists of a Transformer encoder and an auto-regressive Transformer decoder, which we train for frame-level and segmentlevel predictions, respectively. For duration prediction we use an alignment decoder – followed by cross attention – on top of the encoder and decoder features to compute a frames-to-segment assignment, which is used to compute the durations of the segments. + +already available from the previous encoder and decoder. During inference, we compute the segment durations by taking the sum over the assignments: + +$$u_i = \sum_{t} \overline{M}_{t,i},\tag{7}$$ + +where i ∈ {1, ..., n} and Mt,i denotes whether frame t is assigned to segment i. We found that training the alignment decoder using only the loss for M (7) in a separate stage on top of the frozen encoder and decoder features results in a more robust model that suffers less from overfitting. + +In this section, we show how our proposed framework can be extended to the timestamp supervised setting. In this setting, we are given a single annotated frame for each segment in the video, *i.e.,* frame annotations are reduced dramatically, and ground-truth segment durations are no longer available for all frames. As we extensively discussed before, our proposed framework relies on the frame-level supervisory signal on top of the encoder. However, it turns out that a noisy frame-level annotation provides a solid signal to the encoder. To obtain such frame-level annotations, we propose a constrained k-medoids algorithm that propagates the timestamp supervision to all frames. + +A typical k-medoids algorithm starts with random data points as the cluster centers. It iteratively updates the cluster centers chosen from the data points and the assignments based on their similarity to the cluster center. Having access to the timestamp supervision, we can use them as initialization and cluster the input features. However, in a standard k-medoids algorithm, a temporally continuous set of clusters are not taken for granted. We call our method constrained k-medoids because we force the clusters to be temporally continuous. This can be simply achieved by modifying the assignment step of the k-medoids algorithm. Instead of assigning pseudo-labels to each frame, we find the temporal boundaries of each cluster. In the assignment step, we update the boundaries such that the accumulative distance of each cluster to the current center is + +![](_page_8_Figure_2.jpeg) + +Fig. 3. Constrained K-medoids. Given frame-wise features and timestamps, k-medoids generates a pseudo-segmentation that guides the encoder during the training instead of ground truth frame-level labels in a fully supervised setup. + +minimized. Alg. 1 summarizes the steps of our clustering method. In principle, we can apply k-medoids using the frame-wise input features xt, the encoder features et, or a combination of both. In practice, we found that using input features alone gives surprisingly accurate segmentations, see Table 3 or supplemental material for more analyses. diff --git a/2210.13611/main_diagram/main_diagram.drawio b/2210.13611/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..14c5571bce7be1fa12135d83a976e46e136ea477 --- /dev/null +++ b/2210.13611/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2210.13611/main_diagram/main_diagram.pdf b/2210.13611/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..28f0fb955b39096ec669f076b29800d18dd8954e Binary files /dev/null and b/2210.13611/main_diagram/main_diagram.pdf differ diff --git a/2210.13611/paper_text/intro_method.md b/2210.13611/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..e42475a2883fe91ef23ee82cbe66a476d02ac8a7 --- /dev/null +++ b/2210.13611/paper_text/intro_method.md @@ -0,0 +1,32 @@ +# Introduction + +Deep reinforcement learning (RL) utilizes neural networks to represent the policy and to train this network to optimize an objective, typically the expected value of time-discounted future rewards. Deep RL algorithms have been successfully applied to diverse applications including robotics, challenging games, and an increasing number of real-world decision-and-control problems [François-Lavet et al., 2018]. For a given choice of task, RL algorithm, and policy network configuration, the performance is commonly characterised via the learning curves, which provide insight into the learning efficiency and the final performance. However, little has been done to understand the detailed structure of the state-to-action mappings induced by the control policies and how these evolve over time. + +In this work, we aim to further understand deep feed-forward neural network policies that use rectified linear activation functions (rectifier linear units or ReLUs). ReLUs [Nair and Hinton, 2010] are among the most popular choices of activation functions due to their practical successes [Montúfar et al., 2014]. For RL, these activations induce a piecewise linear mapping from states to actions, + +![](_page_1_Picture_0.jpeg) + +Figure 1: Schematic illustration of a trajectory traversing the piecewise linear regions in the policy state space. S0 and Sk indicate the initial and final states of the trajectory. + +where the input space, i.e, the state space, is divided into distinct *linear regions*, where within each region, the actions are a linear function of the states. Note that the regions are formally an affine function of the input, due to the constant-valued bias terms. For simplicity and convenience, these are more commonly described as linear regions. Figure 1 provides a schematic illustration of these regions, along with a policy trajectory. + +The number of distinct regions into which the input space is divided is a natural measure of network expressivity. Learned functions with many linear regions have the capacity to build complex and flexible decision boundaries. Thus, the problem of counting the number of linear regions has been extensively studied in recent literature [Montúfar et al., 2014, Raghu et al., 2017, Hanin and Rolnick, 2019a, Serra et al., 2018]. While the maximum number of regions is exponential with respect to network depth [Montúfar et al., 2014], recent work has demonstrated that the number of regions is instead typically proportional to the number of neurons [Hanin and Rolnick, 2019a]. + +For RL, we are interested in the local granularity (density) of linear regions along trajectories arising from the policy. Fine-grained regions afford fine-grained control, and thus we may hypothesize that region density *increases* in regions frequently visited by the policy, in order to afford better control. Recent work in supervised-learning of image classification is inconsistent with regard to findings about the region density seen in the vicinity of data points, with some reporting a decrease [Novak et al., 2018] to provide better generalization and robustness to perturbation, and others not [Hanin and Rolnick, 2019a]. For the RL setting, we note that counting regions visited along an episode trajectory arguably provides a meaningful and task-grounded measurement in contrast to line-segments and ellipses that pass through randomly sampled points sampled from training data, which have been used in the prior works mentioned above. We further note that piecewise-affine control strategies are commonly designed into control systems, e.g., via gain scheduling. Understanding how these regions are designed and distributed by deep RL thus helps establish bridges with these existing methods. + +To the best of our knowledge, our work is the first to investigate the structure and evolution of linear regions of ReLU-based deep RL policies in detail. We seek to answer several basic empirical questions : + +- Q1 Do findings for network expressivity, originally developed in supervised learning settings, apply to RL policies? How are the region densities affected by the policy network configuration? Do deeper policy networks result in finer-grained regions and hence an increased expressivity? +- Q2 How do the linear regions of a policy evolve during training? Do we see a significantly greater density of regions emerge along the areas of the state space frequently visited by the episodic trajectories, thereby allowing for finer-grained control? Do random-action trajectories see different densities? + +The key results can be summarized as follows, for policies trained using proximal policy optimization (PPO) [Schulman et al., 2017], and evaluated on four different continuous control tasks. Q1: There is a general alignment with recent theoretical and empirical results for supervised learning settings. Region density is principally proportional to the number of neurons, with an additional small observed increase in density for deeper networks. Q2: Only a moderate increase of density is observed during training, as measured along fixed final-policy trajectories. Therefore, the complexity of a final learned policy does *not* come principally from increased density on-and-around the optimal trajectories, which is a potentially surprising result. In contrast, as measured along the evolving current-policy trajectories, a decrease in region density is observed during training. Across all settings, we also observe that the region-transition count, as observed during fixed time-duration episodes, grows during training before converging to a plateau. However, the trajectory length, as measured in the input space, also grows towards a plateau, although not at the same rate, and this leads to variations in the mean region densities as observed along current trajectories during training. + +# Method + +To test how our findings generalize to RL algorithms other than PPO, we repeat our experiments by training deep RL policies with stochastic actor-critic (SAC) [Haarnoja et al., 2018] algorithm. The experimental setup for SAC and its results are documented in detail in Appendix F. Region densities observed early on during training are quite different for SAC than they are for PPO, exhibiting high observed densities which then rapidly drop, before rising again like the PPO case. We hypothesize this is due to a combination of (i) the entropy bonus for SAC, which is then annealed away, and (ii) the different network initialization used for the baseline SAC and PPO implementations. Despite this initial difference, the evolution pattern of densities are consistent with the PPO results later on during the course of training. Another observation is that similar to PPO results, densities over fixed, current and random-action trajectories are within the same range. This supports our surprising finding that the complexity of RL policies is not principally captured by increased density on-andaround the optimal trajectories. + +We examine whether a non-cyclic task, such as LunarLander, exhibits different linear-region evolution behavior compared to our four default tasks, which are naturally biased towards cyclic locomotion. From the results which are available in Appendix H, we can see that the transition counts and densities along the fixed trajectory are similar in structure to those of the cyclic locomotion tasks, showing that a gradual increase in density appears to be a general property for the PPO setting, even for non-cyclic tasks. + +To study the difference between the value space and policy space, we repeat our experiments on the value networks trained on HalfCheetah with PPO. Full set of results for this experiment are available in Appendix I. Our results show that linear regions in the value functions largely evolve in a very similar way to those in the policy. + +Deep RL provides us with a grounded setting to explore the impact of non-IID data on decision regions. To test if non-IID setting makes a difference, we repeat our experiments by training networks with behavior cloning (BC) using expert data from previously trained policies with PPO on HalfCheetah. The experimental setup for BC and the full results are documented in detail in Appendix J. These results show that the evolution of region densities is different for BC than they are for PPO as the general increase in densities is not observed. In addition, number of transitions, the density values and length of current trajectories are significantly smaller for BC-trained policies. We have two hypotheses for these differences: (i) Policy's learning history plays a significant role in the evolution of regions, as early trajectories inform the template cell divisions that later evolve with further training. Since BC policies observe the entire state space from the moment training starts, they may be able to better divide their state space and avoid adding too much granularity to certain areas. (ii) Network initialization largely affects the resulting linear regions and their evolution during training. diff --git a/2210.13918/main_diagram/main_diagram.drawio b/2210.13918/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..afe14bbe25728848171539dfdd57c689beb312a1 --- /dev/null +++ b/2210.13918/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2210.13918/main_diagram/main_diagram.pdf b/2210.13918/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5f5860ce42018f5893ca9960dcc80ddb4563416d Binary files /dev/null and b/2210.13918/main_diagram/main_diagram.pdf differ diff --git a/2210.13918/paper_text/intro_method.md b/2210.13918/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..e80d33fdaad49146e5cbb08ca60902a96b06e24e --- /dev/null +++ b/2210.13918/paper_text/intro_method.md @@ -0,0 +1,95 @@ +# Introduction + +Rapid advancements in the field of deep learning and natural language processing (NLP) have enabled companies, public institutions and researchers to extract information and gain knowledge from large-scale data generated by individuals. In many cases, it is desirable to share such data with third parties, for example when analyses are performed by external consultants or in order to provide high quality benchmarks for the research community. This, however, entails a variety of risks related to privacy that cannot merely be solved by pseudonymization: A variety of deanonymization attacks enable the re-identification of individuals from tabular data such as movie ratings [@netflix-narayanan], geolocation data [@deanonymization-social-lee] and notably also text [@koppel2009computational; @shrestha-etal-2017-convolutional; @fabien-etal-2020-bertaa]. It is therefore highly desirable to develop anonymization mechanisms enabling secure data sharing, ideally with mathematical privacy guarantees as granted by differential privacy (DP) [@dwork2014algorithmic]. + +
+ +
Main idea of our paper: To share potentially sensitive datasets with third parties, we train a language model (LM) on the sensitive data in a differentially private manner and consequently prompt the LM to generate synthetic samples with privacy guarantees.
+
+ +Existing approaches anonymize every text sample individually by obtaining differentially private vector representations [@syntf-weggenmann; @fernandes2019generalised] or using sequence-to-sequence approaches that rewrite a given sample to eliminate user-revealing information [@a4nt-shetty; @feyisetan-leveraging; @feyisetan-textual; @dp-vae-weggenmann], thereby following local differential privacy. As pointed out by @mattern-limits, local DP requires a very high degree of noise which often leads to incoherent language and only little semantic overlap. The strict requirements of local DP are, however, not necessary if we assume that an entity aiming to share data already has access to the full collection of user-written texts and only wants to release an anonymized version of it. + +In this paper, inspired by recent advances demonstrating the feasibility of training large language models (LLMs) in a differentially private manner [@li2021large], we propose a globally differentially private data release mechanism relying on the generation of a \"twin\" dataset of the original, sensitive user data from large language models. As depicted in Figure [1](#fig:overview){reference-type="ref" reference="fig:overview"}, we train GPT-2 [@radford2019language] to generate texts of our original dataset based on prompts inferred from the sample's individual attributes such as sentiment or topic. For fine-tuning, we use a differentially private optimization algorithm in order to protect the content of our training data. Subsequently, we sample from the trained model to generate a large number of synthetic, anonymous texts, resulting in a verifiably private \"twin\" dataset. We carefully evaluate our proposed method using popular NLP datasets such as IMDb movie reviews or Amazon product reviews. Here, we find that even after learning with strong privacy guarantees such as $\epsilon = 3$ or $\epsilon = 8$ from only a very limited amount of training samples such as 25 or 50, our generated data is of high quality and the classifiers trained on it achieve accuracies only $\sim$``{=html}3% lower than those trained on the full original dataset containing thousands of samples. Notably, we also find that transformer based classification models trained on private data outperform models trained on real data with differentially private optimization. Finally, we show that the differentially private fine-tuning procedure effectively minimizes the risk of data leakage from language models that was previously discovered by @lm-extractdata. + +Differential privacy (DP) is a formal notion of privacy that is currently considered the state-of-the-art for quantifying and limiting information disclosure about individuals. It has been introduced by @dwork2006calibrating under the name *$\epsilon$-indistinguishability* with the goal of giving semantic privacy by quantifying the risk of an individual that results from participation in data collection. + +In the original, *central model* of DP, we consider *adjacent* datasets that differ by at most one record (i.e., one individual's data). A differentially private query on both databases should yield matching results with similar probabilities, i.e., answers that are probabilistically *indistinguishable*. This is achieved via random mechanisms that return noisy query results, thus masking the impact of each individual. + +::: {#def:diffpriv .definition} +**Definition 1**. *Let $\epsilon > 0$ be a privacy parameter, and $0 \leq \delta \leq 1$. A randomized mechanism $\mathcal{M}$ on $\mathcal{X}$ fulfills *$(\epsilon,\delta)$-DP* if for any pair of adjacent inputs $\boldsymbol{x},\boldsymbol{x}' \in \mathcal{X}$, and all sets of possible outputs $Z \subset \mathop{\mathrm{supp}}\mathcal{M}$, $$\begin{align} + \mathrm{Pr}\left[ \mathcal{M}(\boldsymbol{x}) \in Z \right] \leq e^\epsilon \cdot \mathrm{Pr}\left[ \mathcal{M}(\boldsymbol{x}') \in Z \right] + \delta + ~. +\end{align}$$* +::: + +In the *local model* [@duchi2013local], noise is added locally at the data source, before the data is collected and stored in a central database. A basic example is randomized response [@warner1965randomized], where each survey participant either provides a truthful or a random answer depending on the flip of an (unbiased) coin. The local model makes the strong assumption that any two inputs are considered adjacent, which often makes it difficult to achieve a satisfying privacy-utility trade-off. + +An important application of DP is privacy-preserving machine learning to protect the privacy of the training data. Typically, neural networks are trained by optimizing a loss function using stochastic gradient descent (SGD) or a derived method such as Adam [@kingma2014adam], which iteratively compute gradients of the loss function over batches of samples from the training dataset. As shown by @song2013stochastic [@bassily2014private; @abadi2016learning], it is possible to implement a differentially private version of SGD (DP-SGD) by clipping the gradients and applying the Gaussian mechanism [@dwork2014algorithmic]: The latter works by applying noise from an isotropic Gaussian distribution $\mathcal{N}(\mathbf{0},\sigma^2\mathbf{I})$, where the standard deviation $\sigma$ is derived based on the desired privacy parameters $\epsilon$ and $\delta$. + +To achieve good privacy-utility trade-offs, it is important to accurately track the total privacy budget spent throughout the entire training. In the context of DP, repeated executions of the same (here: Gaussian) mechanism is referred to as *composition*. Basic [@dwork2006our] and various more refined, advanced *composition theorems* [@dwork2010boosting; @dwork2016concentrated; @bun2016concentrated] have been stated in the literature that aim at providing tight bounds for the overall privacy budget. However, these advances still resulted in relatively loose bounds and thus large overall privacy budgets over the course of highly iterative algorithms such as DP-SGD. Tight worst-case bounds for composition were derived by @pmlr-v37-kairouz15, however, it was shown to be computationally infeasible to compute them in general [@murtagh2016complexity]. + +For this reason, specific efforts have been made to find tighter bounds and accurate approximations for the overall privacy loss: A first example that provides substantial reduced upper bounds is the moments accountant [@abadi2016learning], which is closely related to Rényi DP [@mironov2017renyi], a generalization of DP based on Rényi divergence. Gaussian and $f$-DP [@dong2019gaussian] provide an approximation of the total budget using the central limit theorem (CLT). Finally, @gopi2021numerical [@koskela2020computing], inspired by @sommer2019privacy, are able to compute the exact budget numerically up to arbitrary precision by aggregating the *privacy loss random variable* with fast Fourier transform. + +# Method + +We consider the following scenario to motivate our approach: an entity wants to implement NLP pipelines to gain insights from internal data, e.g., emails from customers. To seek advice and get support for modeling the data and building pipelines, the entity aims to share an excerpt of the internal data with a third party such as a consultant or a group of researchers. In order to do this without compromising the privacy of its customers, the aim is to synthesize a verifiably private "toy" dataset that reflects the properties of the original data without leaking private information. On such a toy dataset, a third party could research how to best solve the task at hand and train a model to perform inference on the actual internal data, without being able to access sensitive information about customers. Formally, we aim to achieve the following goal: We consider a dataset consisting of a training set $\mathcal{D}_{\mathrm{train}}$ and test set $\mathcal{D}_{\mathrm{test}}$. Given $\mathcal{D}_{\mathrm{train}}$ or a subset of it, we want to train a generative model to synthesize a dataset $\widetilde{\mathcal{D}}_{\mathrm{train}}$ that does not leak information from the original $\mathcal{D}_{\mathrm{train}}$. Furthermore, the synthesized dataset should share statistical properties with the original one so that a classification model trained on $\widetilde{\mathcal{D}}_{\mathrm{train}}$ performs as well as if it was trained on $\mathcal{D}_{\mathrm{train}}$ when making predictions about $\mathcal{D}_{\mathrm{test}}$. + +To achieve this, we use the pretrained autoregressive transformer model [@NIPS2017_attention] GPT-2 [@radford2019language] and use natural language prompts to enable the conditional generation of text based on desired textual attributes such as its sentiment, domain or genre provided in the prompt. Furthermore, we introduce a new training objective that penalizes the generation of samples fitting another label to reduce the risk of faulty labeled samples in our synthetic dataset. Finally, we fine-tune our model using a differentially private optimizer to provide privacy guarantees for our training data and to prevent information leakage from our model when subsequently sampling our synthetic dataset. + +As we want to control specific textual attributes of our synthetic data, we need to train our model in a manner that allows us to generate different types of texts corresponding to the desired attributes or labels present in our dataset. We consider a text sample to correspond to a set of $M$ attributes of interest, namely $A := \{a_1, a_2, \dots, a_M\}$, where each attribute $a_j$ can take on a set of categorical values $C_j$. In the case of product reviews, $a_1$ could be the sentiment of a review that can take on the values $a_1\in C_1 = \{\mathrm{Positive}, \mathrm{Negative}\}$ and $a_2$ can be the product category, so that $a_2 \in C_2=\{\mathrm{Books}, \mathrm{Electronics}, \mathrm{DVD}, \mathrm{Kitchen}\}$. Our goal is to learn a model $p(x|a_1, ...,a_M)$ in order to controllably synthesize text samples according to our desired attributes. + +A straightforward approach to realize this would be to train a single generative model for all possible attribute value combinations. This approach is, however, highly memory-intensive, as it requires us to store the weights of a large number of models that grows exponentially with the number of categorical attributes. Following recent work [@schick-schutze-2021-shot], we therefore train a single language model to conditionally generate texts based on task instructions. Beyond reducing our memory needs, this approach allows us to leverage our model's pretraining knowledge and to perform text generation with only very little training samples [@schick-schutze-2021-shot]. Our instructions $\bm{i}(a_1, .., a_M)$ are formed using a template with placeholders that are filled out with verbalizations $v(a_j)$ taking on different forms for different values of every attribute $a_j$. An example of such an instruction template is visualized in Figure [2](#fig:prompts){reference-type="ref" reference="fig:prompts"}. + +During the training stage, we use a differentially private optimizer to fine-tune our language model to generate each text sample within the original dataset based on the prompt corresponding to its individual attributes. Subsequently, we can synthesize a new dataset by controllably sampling text based on our desired attributes passed in the prompt. To generate a private \"twin\" dataset, one might use the same distribution of textual attributes as in the original dataset. Alternatively, the instruction-based approach allows us to control and change such ratios, for instance if we desire to debias our original data. + +
+ +
Our template-based approach for generating task instructions. A template consists of placeholders for verbalizations of different attribute values.
+
+ +The standard training objective for autoregressive language modeling is to minimize the negative log-likelihood (NLL) of every token given its previous tokens. We incorporate the natural language instructions [@radford2019language; @NEURIPS2020_1457c0d6] into this training objective. For every text sequence $\bm{x}$ and its corresponding attribute values $a := (a_1, ..., a_M)$, we construct the concatenated sequence $\bm{i}(a) \oplus \bm{x}$ which prepends a corresponding task instruction to each text sample. Let $L$ denote the length of this concatenated sequence and let $w_l$ be the sequence's $l$-th token. Our NLL loss is now $$\begin{align} + \mathrm{NLL}(\bm{i}(a) \oplus \bm{x}) = - \sum_{w_l \in \bm{i}(a) \oplus \bm{x}} \log p({w}_l|w_{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 \ No newline at end of file diff --git a/2211.14391/main_diagram/main_diagram.pdf b/2211.14391/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c1fa565c0ad05c6ee7b1c0cffed92137d0bbf839 Binary files /dev/null and b/2211.14391/main_diagram/main_diagram.pdf differ diff --git a/2211.14391/paper_text/intro_method.md b/2211.14391/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..a60ccd6999920a2be7a641f919983fe54af1b186 --- /dev/null +++ b/2211.14391/paper_text/intro_method.md @@ -0,0 +1,87 @@ +# Introduction + +With recent advances in mobile devices' hardware and the need for intelligent tasks in mobile applications, federated learning (FL) has emerged as a new learning approach [@mcmahan2017communicationefficient] allowing learning from distributed data scattered across multiple clients while preserving their privacy. + +A typical FL process consists of multiple rounds of communication between one server and multiple clients. The server broadcasts its most recent global model to the clients in each round. Each client trains the model locally for several epochs and sends its updated trained model parameters to the server. The server collects all updated models received and aggregates them into a new global model for broadcast in the next round. Privacy is preserved as the server only exchanges models with its clients instead of training data, making FL a perfect fit for machine learning scenarios requiring privacy. Recent studies suggest that FL is used in two main settings: cross-device and cross-silo [@kairouz2021advances]. In cross-device FL, clients are typically edge devices with limited resources and might not always be available. This study focuses on the reliability and availability concerns of edge devices and discusses the differences between the two settings in the background section. + +In cross-device FL, only a fraction of clients gets selected for training in each round to save communication costs and avoid unavailable clients. This introduces a critical topic: client selection strategy [@mcmahan2017communicationefficient; @kairouz2021advances]. Vanilla FL selects clients randomly between available clients, which has its drawbacks. First, clients have heterogeneous resources which vanilla FL ignores. Second, client availability history is an important factor that should be considered in the selection phase, and vanilla FL does not include that either. In reality, some clients are slower than others, and some are less reliable [@hetersurvey; @imteaj2021survey]. Considering the resource heterogeneity to make the convergence faster, new client selection techniques have been proposed [@fedcs; @tifl; @oort; @powerofchoice; @efficienyselection]. However, none of the proposed techniques consider availability history as a deciding factor. In the FL context, availability is of paramount importance [@hetersurvey]. If a client's availability fluctuates and becomes unavailable in the middle of a training process, the training will likely fail to complete. In a real-world scenario, a timeout is needed to prevent training from getting stuck [@bonawitz2019towards]. Therefore, availability, like resource heterogeneity, can impact the training time for FL and must be considered in FL applications. + +In this paper, we create three scenarios using different availability settings, namely high, low, and average (for instance, in a low availability scenario, most of the clients are unreliable and unavailable most of the time). In each setting, a mix of clients with different levels of availability are selected from 100k real-world mobile device traces with heterogeneous resource capacities [@oort]. We then propose Must Detect the Availability (MDA), an availability-aware client selection strategy aiming to improve FL's performance with availability constraints. We implement these simulations in FedML, an open-source FL framework [@fedml]. We run our experiment on two of the most popular datasets used in FL research papers: CIFAR-10 [@cifar] and FEMNIST [@femnist]. + +To show the importance of availability in the training time of FL, we first study the effect of the three availability scenarios in vanilla FL with random client selection. The results show that a low-availability setting can slow down learning time by up to 10% compared to a high-availability setting, meanwhile degrading the accuracy by up to 5%. We then compare our proposed approach to random client selection used in vanilla FL in average- and low-availability scenarios. The results show that MDA can reduce the training time by up to 6.5%, as well as reduce the number of timed-out rounds by up to 38%. + +To see how our approach works compared to state-of-the-art client selection techniques, we implement two existing client selection techniques proposed in the literature: FedCS [@fedcs] and TiFL [@tifl]. + +The results show that TiFL is more effective than FedCS in improving the training time and gets better results than MDA alone. This is because MDA only considers availability as a selecting factor which has a direct impact on the number of *failed rounds* and an indirect impact on the time. Thus it performs as well or better than baselines in terms of failures but cannot be as fast as them. Since the state-of-the-art techniques select faster clients and directly impact the training time, they are more powerful. However, since availability and resource factors are not overlapping, combining them could produce better results. Thus we combine TiFL (which shows to be the better option between the baselines) and our approach and call the new technique TiFL-MDA. The results show that TiFL-MDA improves TiFL's speed by up to 16% and outperforms all the state-of-the-art techniques, and is the fastest in all cases. + +Our contributions are summarized as follows: + +- We propose an availability-aware client selection for FL called MDA. To the best knowledge, we are the first to consider availability in client selection strategies. + +- We conduct a complete analysis of client selection techniques in heterogeneous resources and different availability scenarios. + +- We propose an availability- and resource-aware technique called TiFL-MDA that outperforms all the baseline client selectors. + +In the remainder of this paper in Section [2](#background){reference-type="ref" reference="background"}, we discuss the required background to understand this paper. Section [3](#mda_section){reference-type="ref" reference="mda_section"} covers our proposed technique. Section [4](#experiments){reference-type="ref" reference="experiments"} discusses our goals, experiment design, and results. In Section [5](#related_work){reference-type="ref" reference="related_work"}, we cover the most relevant studies, and finally, we conclude our study in Section [6](#conclusion){reference-type="ref" reference="conclusion"}. + +This section describes the process of FL in ideal conditions and the challenges it has to face in real-world scenarios. Moreover, we discuss the details of the client selection techniques used in this study. + +![Federated Learning workflow diagram. The orange arrows represent the global model broadcast to the clients, and the black arrows represent client updates sent to the server. Each device has a dataset and a locally trained model, which are represented by different colors.](FL_workflow_rel.drawio.png){#fl_fig width=".9\\linewidth"} + +Federated Learning (FL) is a new technique introduced in recent years [@mcmahan2017communicationefficient]. FL leverages all clients' data to perform distributed learning while ensuring clients' privacy. Figure[1](#fl_fig){reference-type="ref" reference="fl_fig"} shows how FL works in practice. FL is an iterative process, and each iteration is called a training round. Each round starts when the server broadcasts a global model to the clients. When a client receives the global model, it performs local training using its data for a few iterations, called local epochs. After a client completes local training, it calculates the difference between the global model received from the server and the trained model. Then it sends the difference called \"delta\" back to the server. Once the server has all deltas, it will aggregate them to update the global model. This process repeats until the global model converges to a desirable accuracy or the training reaches a certain number of rounds. + +This process of FL only transfers model parameters and the delta (difference between global and local models) between clients and the server. Data resides on clients' devices and is never sent to another client or the server, preserving privacy. + +FL was originally designed for mobile devices. Recently, it is applied in other privacy-preserving scenarios. FL can be categorized into two main settings: cross-device and cross-silo [@kairouz2021advances]. In cross-device FL, which is the focus of our study, clients participate in the training process. They are limited in computational and communication resources. They are not reliable and can become unavailable for training. In this setting, only a fraction of clients gets selected for training at each round (The client selector component in Figure[1](#fl_fig){reference-type="ref" reference="fl_fig"} does this task). In comparison, cross-silo FL deals with only a few clients, but these clients are highly reliable and powerful. All clients participate in training each round. An example of cross-solo FL is among multiple data centers belonging to different hospitals. + +One of the main challenges in FL (both settings) is the data heterogeneity of clients [@mcmahan2017communicationefficient; @flsurvey; @flsurvey2; @kairouz2021advances]. In a real-world case, data distribution is not independent or identically distributed (non-iid), which can cause problems for the FL convergence process. Federated Averaging (FedAvg) is shown to be an effective aggregation technique in non-iid cases [@mcmahan2017communicationefficient]. FedAvg is the baseline aggregation method in FL, and it is what we use in this study. FedAVg simply takes the mean value of client updates and uses that to update the global model. + +The availability of clients and resource heterogeneity [@hetersurvey; @fedcs; @imteaj2021survey] remains a vital challenge in cross-device FL, where clients are less reliable. In this setting, FL uses a client selection technique to select a fraction of clients at each round to make the process withstand the availability problems and reduce communication costs. We will discuss the client selection techniques in the next section. + +**Random client selection** [@mcmahan2017communicationefficient]: Client selection is performed randomly in vanilla FL. At each round, the server pings the clients to see which ones are available, creating a pool of available clients. Then the server selects a fraction of them randomly from the pool. Random client selection is primitive as it only considers availability as a state and does not consider the history of availability, which can be very important. Moreover, resource heterogeneity is not considered. Slow clients may be selected and become the bottleneck of the round. + +**FedCS client selection** [@fedcs]: This technique is the first and most popular client selection technique that considers resource heterogeneity in its selection process. Like random client selection, it first finds the available clients and selects a fraction of them randomly. Additionally, FedCS has a threshold hyper-parameter and will prevent slow clients from being selected for training. So after the random selection, it performs filtering based on that threshold and only selects a subset of the clients. This parameter determines how fast the overall training will be. + +To find clients' required time for training, FedCS gathers information on clients' resources and estimates the time for each client based on model complexity and the client's resources. The main issue with FedCS is that it excludes many clients for faster training. In non-iid cases, this could have detrimental impacts on the model's accuracy. + +**TiFL client selection** [@tifl]: TiFL (Tier-based Federated Learning) is the last selection technique we study in this paper. Like FedCS, TiFL is a resource-aware technique that aims to speed up the training process. TiFL starts by putting clients in different tiers based on their speed/resource, gathered before the training process starts. The number of tiers is an important parameter that needs to be selected carefully for the best results. After tier initialization, in each round, TiFL selects a tier and then selects the desired number of clients only from clients in the selected tier. Tier selection is another important factor in TiFL, and it is performed in a way that faster tiers get selected more than slower tiers. The reason behind the success of TiFL is two-fold. First, it selects faster tiers more often than slower tiers meaning TiFL is not excluding slow clients, unlike FedCS. Rather, it selects them less often, which makes it fairer. Second, selected clients in a round are from the same tier, so a slow client is never selected with a fast client, therefore avoiding bottlenecks. + +In this section, we cover the importance of availability in client selection and propose the MDA client selection technique, the first availability-aware client selector to the best of our knowledge. Finally, we propose TiFL-MDA, a combination of TiFL with our MDA technique to have both availability and resource heterogeneity in mind. + +Currently, all existing client selectors either do not consider client availability or only consider it as a state during the selection phase [@mcmahan2017communicationefficient; @hetersurvey; @fedcs; @tifl; @powerofchoice; @oort]. That means the selector pings all the clients to find available clients at each round and then selects from the available client subset. However, they do not consider the availability history of the clients. For instance, if a client's availability fluctuates and the client gets selected for training, it will likely become unavailable and fails to finish the training process since a real-world FL system must enforce a timeout for each round to withstand potential failures [@bonawitz2019towards]. + +Timed-out rounds caused by availability fluctuations are never desirable since they slow down the learning process, wasting the resources and energy of the failed clients. An intelligent client selection technique is desired to reduce the occurrence of failed training for more effective model updates. + +# Method + +:::: algorithm +::: algorithmic +$C, A, F, r, n, m$ + +$UpdateHistory(A, F)$ + +$Candiadates \leftarrow GetAvailableClients(C, r)$ + +$W \leftarrow []$ $W[c] \leftarrow 0.5$ + +$totalTime \leftarrow 0$ $availableTime \leftarrow 0$ $elapsedTime \leftarrow CalculateElapedTime(i-1, i)$ $totalTime \leftarrow totalTime+elapsedTime$ $availableTime \leftarrow availableTime+elapsedTime$ $W[c] \leftarrow availableTime/totalTime$ + +$pen \leftarrow 0$ $maxPen \leftarrow 0$ $p \leftarrow 1/(r-i)$ $maxPen \leftarrow maxPen+p$ $pen \leftarrow pen+p$ $W[c] \leftarrow W[c]*(1-pen/maxPen)$ $W \leftarrow NormalizeProbabilities(W)$ $S \leftarrow WeightedRandomSelection(Candiadates, n, W)$ $S$ +::: + +[]{#mda_algorithm label="mda_algorithm"} +:::: + +To reduce the chances of client failures, our approach considers two main factors per client: availability history and failure history. These historical factors are initially empty lists and are progressively filled as training progresses. The availability history for a client is defined as a list indexed by the number of rounds. The values in the list are Boolean, indicating the client was available at a specific round. The failure history is also a list, but it only contains the indexes of the rounds that the client had failed in it before, or empty in case the client had not failed in any of the prior rounds. + +Algorithm [\[mda_algorithm\]](#mda_algorithm){reference-type="ref" reference="mda_algorithm"} shows how MDA selects clients at each round. The process starts by updating the history of clients (availability and failure) based on the information available to the selector. Following the original FL client selector, [@mcmahan2017communicationefficient], MDA filters clients to gather only available clients in Line 2, then initiates a list of weights $W$ of the size of *Candiatates* and iterates through *Candiatates* in Line 4. + +Lines 6 to 17 show how MDA utilizes the availability history to alter the selection weight for each client. MDA looks at the *m* most recent items in the availability history to calculate the percentage of time that the client was active. Since the exact time of availability change is unavailable to the server and it only knows if a client was active at a certain round, MDA estimates availability time in Lines 12 and 13. MDA considers the client to be available between two consecutive rounds if it was available in both. In other cases, MDA assumes the client was unavailable in that interval. Then MDA calculates the availability percentage in Line 16. If the history is adequate (Line 6), the client receives a weight between 0 and 1 (the availability percentage). Otherwise, it receives 0.5, which is the default value. + +Lines 18 to 29 show the process of applying the failure history in MDA. MDA penalizes the clients that have had failures, and the penalty is larger if the failure is more recent. There are two critical variables in this part *pen* and *maxPen*. MDA iterates through all rounds from first to last (Line 21). It then calculates the inverse of the distance between that round and the current round and puts it in the variable *p*. The penalty is calculated this way to give more importance to more recent failures. In Line 23, the penalty *p* is always added to *maxPen* since that is supposed to represent the worst case in which the client has been selected every single round till now and failed at all of them. Then MDA checks to see if the client has failed at that round, and only if that is the case, it adds *p* to the *pen*. Finally, MDA calculates the normalized penalty and penalizes the client's weight based on it. + +When the weights are all calculated, in Line 31, MDA normalizes them so that their sum is equal to one, since they represent probabilities. In the end, MDA performs a weighted random selection between all the *Candidates* and returns the selected clients. + +MDA works non-invasively and can be combined with any state-of-the-art client selector to make the resulting selector even more powerful. The resulting selection technique will have both availability and resource heterogeneity in mind. As a result, training time will be faster, and FL will use client resources more efficiently. Given that at least in theory, TiFL is better than FedCS, we combine MDA with TiFL to propose the first resource and availability-aware technique. As discussed, TiFL's theoretical advantage is because FedCS excludes slow clients completely, but TiFL still includes them but less frequently. + +As discussed in Section [2](#background){reference-type="ref" reference="background"}, TiFL first assigns clients to different tiers, then performs the random selection (just like vanilla FL but on a subset of clients) from a certain tier. We introduce TiFL-MDA, which replaces the random selection component of TiFL with MDA. diff --git a/2211.16022/main_diagram/main_diagram.drawio b/2211.16022/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..86fea96467fbd97336d01892c851dd88bf1f20ac --- /dev/null +++ b/2211.16022/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ +7V1bc6O4Ev41rpp5cAokLubRiZ2dOpfdTJKts7MvU8TINrsEHMCJs7/+SCCuagy2wdiJ/ZCAQLLU/fWF7kYe4JvnzS++uVr+17OIM0CStRngyQAhNFJV+o+1vMctGCUtC9+24jY5a3iw/yG8UeKta9siQeHG0POc0F4VG2ee65JZWGgzfd97K94295zit67MBREaHmamI7b+z7bCJW/VVCW78I3YiyX/aoSxFl95NtO744ZgaVreW9wUrQ5PB/jG97wwPnre3BCHkS8hTEyC24qr6cx84oZNOuAfujZZzd40+a+HP7zx739+XwyHshEP82o6a75kPtvwPaGB761di7BR5AG+flvaIXlYmTN29Y2ynbYtw2eHX7bMYJneO/fckPNUVug5/y7ih2RTuQo5pQ2FFfGeSei/01t4h6GOOTQ4pIYyVnjLW8YhTeNtyxxzlARVJkfFIh0+oxs94KTbhYyKQDViUSDxU88Pl97Cc01nmrVeZ3SV6Fl2z388b8UJ+BcJw3dOQXMdekVak40d/sG6X6n87AcfjB1PNvmT9+TEpevNdWKnP5Lx2EnWLTpL+pVZSVxrzOSLns8cMwjsWdx4azvJ9GKSMDps5zUlm7f2Z2QbfTkqQ9NfEN73xf91af9+/b6ZGy//vnlTN2N9MkQweHzimKH9WpxI+yhALQtTmegF4WpDmGQklaVJGonSpCjoShXlCY26kid8OuIkH0Wc9pMVUAQSM1knKjI+UFairlQJmO+5G1ae7YZBbuQ71pCHHNZLkBuVDVe5izE6vIdW02Oko6096EG81AzUKc32x3mvZkPOoTzDfB3O8yjPgf50cd6rSVAviqx7RaZcFFluWpJxeBdDkrtXfqPTkY39fGbppGTjbXztv3yfD8fKj9mjtLn7+9urPKzw/44uGjLGNSDUkshAVZduQGhcQNg5CA+1wMcDITJ6AWESlzgyCltCVGehgINAdypPN/Wga6WLrOwM7VKXg6G9jV2FkIjmhCyYYb8WIK+9rFko9Dokm3BoOvbCHeAxvWNGIUL8CETJLfRowf878f/baLRiK8Mp+A3swjCIEMy+QMarzdbhW5ypXW5YJw1jd7b0/NyK1sAq893NZxYtcp+CVTtrlmaeQyeQ7zRAWJI0+gEWpN58uaOdovulL48DRPEhTb/S9q+5Ccfz6Zhb8MzZvFl0q4oVT35+LY9s9GjuUsX0bxl3pLnt2sHSdhdMB3nr2ZJQdSD9y1u69J9LCNPIdAg6ovtT4gMuTMfxXHafN4+0XiSZjF72PAqp0T9P3prF1ZKustCVam+qhywrOVzarJW6CEEYzybuiGq/c0k1NZKe6dTZP88n2WXpzY4kOpoRW8wA3+bJBXCnmr7HYyhd+DRlHww9egtbEmUJkobRkZweoa2YrYzfMlNYE7/lumHikHmy8LzhnFNq36QrxVNtIk3oQq6D0Pf+JrkrI/REV59OBzCbQhh4S8xX0crGAwExXzlpy0d8ZU2ttphNAyWgnVDFUPlRXaCCA7RnWGsfd6ZpYiPJepV8qKM7OKroeXDHotLzELsgWR107nmovSYR+guudolCpJ0GCvVywGofFO7RRUJHAG7XWcRWjIhcDkxSI6IKRgQbkBHRuzIiuEPKoUG5uKFovS2TjOYzyHprsxF5mrdEeCEgLCNdpHtK4gLdkzqJ1umufHy6y7JmCITXrkZ6U9rL6b3t+067kP8wtxUBDFDJyFK6d1tlLS1CyFiQVFvl6a8gkf6dqRztc9BeBWiP+qa9DtC+tdAP1WnK3U97kIU42EkU5Phpf42vd/rQmEaTTgUDCoABuW8MjCoxUBW12z3OV90jWJluI5wpFTh79E3bjeM3DyTM4SkeuUng7MBwWtXMvtSHLB/WK+K/2gELeTULWzYOs7DQ6lbFyJBrz0xnzJufbcuKn+AIXZz5FA3EpI0/S9BR1euBOmEj0Ye2gMuTIDau55KSjCVNJTHsTrGO0ra8WEmAK6125dHtVBZ8rmbNEAMToD8BEL4zfZakAj845cUQJOhNHJXy1amqttyJgTod6FT/LAb6pOBalC9Ebkax8RO6HIYqBg57RwkUY/jECc0OEo6YfqoSjpFIfL+nEiFID2/NiU7UcsYZydyytme3upqwQT/whKuSpOW89Lddko0R7xb2K2E+dbg0w/Jo8/NPwZ5PQrUx+s4ht9qbRylp6pWGRaNlQEFCTevKakER2ovVOqrVuv9tMobsVtqes1y87dxtV7K0GuuV193BFtXN7UdZgxctRk/lNKb7XnmbZHmRicpZOqnalJ2ZhWjK44SpzHW4Tq1EcvzJ7IQsj8rZpN0MRWdlOL2+lNFH0UQQmn6Y1C/zUF8li/OFFNVVx03KeTid2yukOIjtaKc0VssZ3Pl8jmZgBtfSnjS1LZlTFaEARALCrCpY9tZVnBVBOawPF+5TcTnGLWkA6RFA+jZeMYcpD2WOPh7lkUB5tXfKQ8mF7S7+Xo8KrT+GRPFX9YYSjoS2YxHq/dwx10edDumIX0qXHpNLkZtYujjlF+uiursn/hqnJOtomPPoq9KSHzDmrMqCwCiAVwYJjD7qyinTP5tT1sgB21onXe+B6SflgeGd8n4fxQNLX0rrzwPDUNrv4/kBggeGevcDMPoUlBc8MKT0TvnqJGanblIc7RnE4U/YXYpuuSknfmDvKboXcFYKXT9h3lz0YRDuHXJiBuLOC+zIsCLpxnQt2zJDdhzVwZW4Ul0OlpK+/4Iwdp5jqRR9ujOdwOsWYCkE6oylYt35r2RhXli6d1AE8oaOy1JN4NOJ7lclxPF35VT9O3GcFj28FHcYD7svi+e2F0/iRB832XHyEn1Giytuo3kC7nV1aXyLOBgmKGBxrmmTkNanYH//8W1cHWWtrujPXLKm25Ccm1Xfy1YcqAmA7amPadQVKLRUh4TMk7sgoT2lICJBPyoSxOf+72sShLbn1tScNH1r6J54vkX8qKKlFhBlNpeY2hwvIj4A3oMYKVofCB6J71u9ZdkOmBhpJZ9/ZBhQ1Qf02nx3b2+Lj+b3hC4q915XFl75bUV8swyY+NKTGfcohF8mZmiKt47Xi2dKRj7QBSjAxuxySXsYEvDqw1E9ClW0I5yPUUVb9JAvibxu8iIhcVi53F7vE54/eNrAi6ELIb8kHVmHlzb0SvXes9vD+3QUexWQese/5NffRp+WaKcjwWWDwqUYkjX5CnVWiifWZE1f1pHKHHJVC0jbPf1um9A+TJ5CqqrJ4v1j+WZ5XYs74r+sQ2/gQgjoTHrQmUgPFl/lhEIfx5YeTXR1H9l7AhfJaVFyAN5jYEOco8pNgz2gTlRuqirAOxQdkIINdnP64OVHuR2td6kIb5xG2AbclrfWA/aOFvbOk8pQihfFO2ZoAjaVFmMc5bHidQtj7bFZHkg1MQd5UfPdq3lJUa/4T4QcRdODu+ZDHhLn9t7Fv1VxrXw2+87znPqC3dwczu8RtQIiO/22nCEUPsnADzWCkMFdQUauzo0+5YMRQzEUIZZDN30flZejbam0hsNiEJRq6sHPGnSd/3SoIZWjrwoQU4MA2cY+S7AOq87NpLx89O2VEwXX7kybolDKmbAL5xtxHpUtmAHlZ6Gs3B6qiJ5mv+ob+znZryPj6f8B \ No newline at end of file diff --git a/2211.16022/paper_text/intro_method.md b/2211.16022/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..f824973907b623edb155ecaefa1f9e19710ad955 --- /dev/null +++ b/2211.16022/paper_text/intro_method.md @@ -0,0 +1,118 @@ +# Introduction + +Solving Math Word Problems (MWPs) is the task of automatically performing logical inference and generating a mathematical solution from a natural language described math problem. Solving MWPs is a challenging task that cannot rely on shallow keyword matching but requires a comprehensive understanding of contextual information. For example, as shown in Figure [1](#fig:ex){reference-type="ref" reference="fig:ex"}, while the first problem shares high token-level overlapping with the third problem, the underlying mathematical logic is different. While on the other hand, the first and second problems have very low similarity at the textual level, while the equation solution is the same. The challenge of the task is that the underlying mathematical logic would change even with minor modifications in the text. While neural network based models have greatly boosted the performance on benchmarks datasets, @DBLP:conf/naacl/PatelBG21 argued that state-of-the-art (SOTA) models use shallow heuristics to solve a majority of word problems, and struggle to solve challenge sets that have only small textual variations between examples. + +
+ +
Example of positive data point P+ = (T+, E+) and negative data point P = (T, E) for an anchor P = (T, E).
+
+ +Motivated by recent progress in contrastive learning methods, which is a flexible framework that has been successfully employed to representation learning in various fields [@chopra2005learning; @DBLP:journals/corr/abs-2005-12766; @DBLP:conf/emnlp/GaoYC21], we propose Textual Enhanced Contrastive Learning, which is an end-to-end framework that uses both textual and mathematical logic information to build effective representations. For each *anchor* data point, we find the *hard example* triplet pair, which consists of a textual-different but logic-similar *positive* data point $P^+$, and a textual-similar but logic-different *negative* data point $P^-$. Our method aims to learn an embedding space where the vector representations of $P$ and $P^+$ in Figure [1](#fig:ex){reference-type="ref" reference="fig:ex"} are mapped close together, since they hold the same mathematical logic even though the textual expression is entirely different; on the other hand, because $P$ and $P^-$ have similar textual expression but different mathematical logic, their vector representations could be separated apart. + +To build such triplet pairs, we use a retrieval-based method to search in the training data. We consider the equation annotation as the representation of the mathematical logic in the example, and retrieve a positive and negative bag of data points according to equation similarity. Then we further use textual similarity to choose the *hard examples* in the bags, where positive examples have low textual similarity with the anchor and vice versa. Given such *hard sample* data, Contrastive Learning could empower the representations by leading the model to distinguish these potential disorienting examples in the training stage. + +Such approaches to retrieving triplet pairs from human-annotated training data via label annotation are considered as *supervised* contrastive learning. Another research line of contrastive learning is *self-supervised* contrastive learning, which does not require labeled data and use data augmentation methods to generate the positive or negative data points [@DBLP:conf/icml/ChenK0H20; @DBLP:conf/cvpr/He0WXG20; @DBLP:conf/nips/GrillSATRBDPGAP20]. In the task of solving MWPs, we can leverage *self-supervised* supervision by generating new examples via performing synchronized changes to text and equations. The generated data is naturally *hard sample* data, because the textual expression is similar to the origin example, while the equation could be either changed or the same. Specifically, we leverage Reversed Operation-based Data Augmentation [@liu2021roda] and a Question Reordering-based augmentation to form new data points. By enhancing the model to detect the small perturbations in the augmented examples, contrastive learning forces the model to learn more effective representations of contextual information. + +While previous studies also used Contrastive Learning to improve representations for solving MWPs [@li2022seeking], their method is limited to supervised contrastive learning, ignores textual information during constructing the contrastive learning pairs and requires two step pre-training and re-training. Our method pushes the model to learn better text representations and understand the most minor textual variance from these textual enhanced *hard samples* from both *supervised* and *self-supervised* perspectives. + +We conduct experiments on two widely used datasets, the English dataset ASdiv-A [@DBLP:conf/acl/MiaoLS20] and the Chinese dataset Math23K [@DBLP:conf/emnlp/WangLS17]. To further investigate how our method improves the ability of the model to detect small textual perturbations, we collect a Chinese challenge set Hard Example (HE)-MWP. We perform experiments on two challenge sets of MWPs, the English Asdiv-Adv-SP dataset [@DBLP:conf/emnlp/KumarMP21] and the Chinese HE-MWP dataset. Experimental results show that our method achieves consistent gains under different languages and settings, demonstrating the effectiveness of our method. + +# Method + +We use Contrastive Learning to obtain text features with high differentiation of small perturbations, so that for each *anchor* data point $P=(T,E)$, where $T$ stands for the text and $E$ stands for the equation, we construct a pair of examples *positive* data point $P^+=(T^+, E^+)$ and *negative* data point $P^-=(T^-, E^-)$, and then use contrastive learning loss to map the representation of $P$ and $P^+$ closer and vice versa. The pipeline of the triplet pairs retrieval is shown in Figure [2](#fig:model){reference-type="ref" reference="fig:model"}. We first construct a candidate pool, which consists of *supervised* training data $\{P_i\}$ and augmented *self-supervised* data $\{P^{aug}_i\}$ as shown in the blue part of Figure [2](#fig:model){reference-type="ref" reference="fig:model"}. The *self-supervised* data is generated by two methods, Reversed Operation based Data Augmentation (RODA) and Question Reordering (QR), which is explained in Section [3.1](#ss:aug){reference-type="ref" reference="ss:aug"}. Then we perform two-step retrieval to retrieve the triplet pairs as described in Section [3.2](#ss:ret){reference-type="ref" reference="ss:ret"}. We first use an equation-based retrieval strategy to extract *positive* candidate set $\{\widetilde{P}^+\}$ and *negative* candidate set $\{\widetilde{P}^-\}$, and then further introduce textual information by choosing one example from the candidate set via a text-based retrieval strategy. Finally, we train the MWP solving model that maps $T$ to $E$ by considering both the contrastive learning and solution equation generation objective, as described in Section [3.3](#ss:model){reference-type="ref" reference="ss:model"}. + +The *self-supervised* examples are challenging for the model to distinguish; while the perturbation in the text expression is extremely subtle, the corresponding mathematical logic could still change. Compared to the supervised examples that are retrieved from the training data, these *self-supervised* samples place a higher demand on the model's ability to detect subtle changes and understand contextual information. We generate task-orientated augmented examples from training set data point $P=(T,E)$ via two methods that obtain reliable new text-equation examples by modifying the text and equation in the same logic at the same time. We split the sentences with punctuation marks to a question followed by various declarative sentences $T=\{S_1, S_2,..., S_{k-1}, Q_k\}$. The question sentence is always the last sentence for Asdiv, and we check whether interrogative pronouns are in the last sentence for Math23K. + +We move the question to the front of the MWP to form a reordered new MWP similar to @DBLP:conf/emnlp/KumarMP21. Given a problem text $T=\{S_1, S_2,..., S_{k-1}, Q_k\}$, we move the question $Q_k$ to the front of the problem text to form a new problem text $T^{QR}=\{Q_k, S_1, ..., S_{k-1}\}$ while the rest of the text remains the same. We simultaneously edit the equation $E^{QR}$ so that the variables match with the new text order. The new example $P^{QR}=(T^{QR},E^{QR})$ could either be a positive example that holds the same equation as $P$ or a negative example that holds a different equation since the variable order might change during the reordering. The high textual similarity but rotated variable order pushes the model to learn representations that can differ from these small textual perturbations. + +We perform RODA [@liu2021roda] that generates a new example by asking a question about one of the original given variable. Given a problem text $T=\{S_1, S_2,..., S_{k-1}, Q_k\}$ where the question $Q$ asks about an unknown variable $n_{ans}$, RODA chooses a known variable $n$ in one of the declarative sentence $S_i$, and then generates a problem text which asks about this variable. To generate such an example, $S_i$ is transformed to a question $Q_{S_i}$ which asks a question of $n$, while $Q$ is transformed to a declarative sentence $S_k$ describing $n_{ans}$. We reorder the problem text by swapping the two sentences, that a new problem text $T^{RODA}=\{S_1, ... S_k, .. S_{k-1}, Q_{i}\}$ is generated. Simultaneously we edit the equation by resolving the equation expression $E^{RODA}$ of $n$ given $n_{ans}$. While $P^{RODA}=(T^{RODA}, E^{RODA})$ has a very similar textual description of $P$, the underlying equation could be completely different, which could benefit the model via contrastive learning. RODA requires text parsing and transformation rules to modify the text and equation. For Chinese, it can cover 93% of the examples, and for English, it covers 60% of the examples. The generated text has a 0.83 out of 1 coherent score reported by human evaluation by @liu2021roda. + +We construct the positive and negative triplet pairs from both textual and logical perspectives. For a given problem $P$, the positive sample $P^+$ is considered to be a problem with similar equation expressions but relatively different text descriptions; the negative sample $P^-$ is considered to be a problem with highly textual similarity but different equation expression. However, it requires a time-consuming bruce-forth enumeration of all possible example pairs to find such optimal positive and negative samples. Considering the computational complexity, we break down the retrieval to a two-step pipeline. we adopt a heuristic searching algorithm to construct positive and negative samples $(P^+, P^-)$ as follows: + +1. Construct a similarity matrix $M$ of all equation expressions $\{E_1, E_2, ...E_n\}$ in the training set, where $M_{ij}$ is the similarity of equation expression $E_i$, $E_j$. + +2. For a given anchor $P$, Retrieve a *positive* candidate set $\{\widetilde{P}^+\}$ and a *negative* candidate set $\{\widetilde{P}^-\}$ of samples from the training set of the data via equation expression similarity. + +3. Extract the best *positive* example $P^+$ and the best *negative* example $P^-$ via textual similarity. + +We investigate various strategies to retrieve $(P^+, P^-)$ from both equation-based and text-based perspectives. + +To evaluate the equation similarity during the retrieval, we design an equation similarity metric $Sim_{eq}$ based on length-wise normalized tree edit distance (TED). TED is defined as the minimum-cost sequence of node operations that transform one tree into another and is a well-known distance measure for hierarchical data. We define the TED of two equation expressions $E_1, E_2$ as the TED of their abstract syntax tree. The similarity of two equation expressions $E_1, E_2$ is defined as: + +$$\begin{equation*} + Sim_{eq}(E_1, E_2) = 1 - \frac{TED(E_1, E_2)}{ |E_1| + |E_2| } +\end{equation*}$$ + +Given this equation similarity metric, we design two retrieval strategies. + +The *positive* candidate set $\{\widetilde{P}^+\}$ is constructed of the examples that meets $Sim_{eq}(E, E_i) = 1$, which means their equation expression satisfies $E = E_j$. If only the *anchor* itself holds this equation expression, the *positive* candidate set $\{\widetilde{P}^+\}$ has only the *anchor* $P$. The *negative* candidate set $\{\widetilde{P}^-\}$ is constructed of the examples that meets $argmax_{E_i \neq E}(Sim(E, E_i))$, which holds the closest equation considering the *anchor*. + +The *positive* candidate set is constructed of the examples that meets $argmax_{E_i, T_i \neq T}(Sim_{eq}(E, E_i))$. If no other example holds the same equation expression as the *anchor*, the *positive* candidate set $\{\widetilde{P}^+\}$ takes the examples that has the nearest neighbour equation expression. The *negative* candidate set $\{\widetilde{P}^-\}$ is constructed of the examples that meets $argmax_{E_i \neq E^+}(Sim_{eq}(E, E_i))$, which holds the closest equation considering the *positive* example. + +The *positive* and *negative* candidate sets are then further screened by the text-based strategy. + +To lead the model to differentiate mathematical logic from similar textual expressions, we use textual-based information to select the $(P^+, P^-)$ pair. We select the **lowest** textual similarity score example from the positive candidate set $\{\widetilde{P}^+\}$, which is the example with different textual expression but the same mathematical logic; and select the **highest** textual similarity score example from the negative candidate set $\{\widetilde{P}^-\}$, which is the example with similar textual expression but different mathematical logic. We design two similarity measurement metrics for this stage. + +Sentence-BERT (SBERT) is a strong sentence representation baseline model [@DBLP:conf/emnlp/ReimersG19]. We calculate the cosine similarity of the SentBERT representation of the two sentences to obtain the similarity score: + +$$\begin{equation*} + Sim^{BERTSim}_{text} = \frac{SBERT(T_1) \cdot SBERT(T_2)}{||SBERT(T_1)|| ||SBERT(T_2)||} +\end{equation*}$$ + +The value range of $Sim^{BERTSim}_{text}$ is from $[-1,1]$. + +BLEU is a widely used evaluation metric for text generation that measures the similarity between the generated text and the reference. We design a Bi-direction BLEU since BLEU is a not symmetrical similarity metric, which is defined as: + +$$\begin{equation*} + Sim^{BiBLEU}_{text} = \frac{BLEU(T_1, T_2) + BLEU(T_2,T_1)}{2} +\end{equation*}$$ + +The value range of $Sim^{BiBLEU}_{text}$ is from $[0,1]$. + +::: table* + **Dataset** Math23K Asdiv-A HE-MWP Adv-Asdiv + --------------------- --------------- --------- ----------- ----------- + Language zh en zh en + Domain general general challenge challenge + #Train 21,162 1,218 \- \- + #Dev/#Test 1,000 / 1,000 \- / - \- / 400 \- / 239 + #Equation Templates 3,104 66 231 66 +::: + +We show the training procedure in Figure [3](#fig:train){reference-type="ref" reference="fig:train"}. The training loss consists of the MWP solving loss $\mathcal{L}_{solver}$ and the contrastive learning loss $\mathcal{L}_{cl}$. + +
+ +
Overview of the training procedure.
+
+ +We follow @li2022seeking and use the strong baseline model BERT-GTS as MWP solving model. The pre-trained language model BERT, which provides strong textual representations, is leveraged as the encoder. For the decoder, we use Goal-driven tree-structured MWP solver (GTS) [@xie2019goal]. GTS directly generates the prefix notation of the solution equation by using a recursive neural network to encode subtrees based on the representations of its children nodes with the gate mechanism. With the subtree representations, this model can well structured information of the generated part to predict a new token. + +Contrastive learning is performed on triplets pairs $(P, P^+, P^-)$ by pulling the representations of $T$ and $T^+$ together and pushing apart the representations of $T$ and $T^-$. We follow the contrastive learning framework in @DBLP:conf/icml/ChenK0H20, which takes an in-batch cross-entropy objective. Let $x_i$ denote the encoder representation of $P$, the training objective for $(x_i, x_i^+, x_i^-)$ within the batch of $N$ triplet pairs is: + +$$\begin{align*} + &\mathcal{L}_{cl}=\\ + &-\frac{1}{N}\sum^N_{i=1}log\frac{e^{cos(x_i, x_i^+)/\tau}}{\sum^N_{j=1}(e^{cos(x_i, x_j^+)/\tau}+e^{cos(x_i, x_j^-)/\tau})} +\end{align*}$$ + +where $\tau$ is the temperature hyperparameter. + +Assume the prediction target equation of $P$ is $y$, the final training objective is to minimize the sum of the MWP solution equation generation negative log likelihood loss $\mathcal{L}_{solver}$ and the contrastive learning loss $\mathcal{L}_{cl}$: + +$$\begin{align*} + \mathcal{L} &= \mathcal{L}_{solver} + \alpha * \mathcal{L}_{cl} + % \\&= \sum_p -log\mathcal{P}(y|p) + \alpha * \mathcal{L}_{cl} +\end{align*}$$ + +::: table* + **Model** Cand. Pool Math23K Asdiv-A HE-MWP Adv-Asdiv-SP + ----------------------------- --------------- --------- --------- -------- -------------- -- + GTS [@xie2019goal] \- 75.6 68.5 \- 21.2 + G2T [@zhang2020graph] \- 77.4 71.0 \- 23.8 + pattern CL [@li2022seeking] train 83.2 \- \- \- + BERT-GTS \- 82.9 73.4 55.5 59.9 + w/ *supervised CL* train 84.1 74.2 57.2 63.7 + w/ RODA CL RODA+train 84.3 74.3 64.1 64.1 + w/ QR CL QR+train 84.2 74.4 62.5 66.2 + w/ CL RODA+QR+train 85.0 74.6 69.5 66.9 +::: diff --git a/2212.02493/main_diagram/main_diagram.drawio b/2212.02493/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..0749a28b8a28a8bfe110092fc320401a3d5555c6 --- /dev/null +++ b/2212.02493/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2212.02493/main_diagram/main_diagram.pdf b/2212.02493/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..08ed19eb0fe139d4a88b640f2ed3d453ce718196 Binary files /dev/null and b/2212.02493/main_diagram/main_diagram.pdf differ diff --git a/2212.02493/paper_text/intro_method.md b/2212.02493/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..0f0cf77578b2679373924444a06387d9cc4bc118 --- /dev/null +++ b/2212.02493/paper_text/intro_method.md @@ -0,0 +1,89 @@ +# Introduction + +Neural fields [\[60\]](#page-10-0)—coordinate-based neural networks that implicitly parameterize signals—have recently gained significant attention as representations of 3D shape [\[6,](#page-8-0) [23,](#page-8-1) [29\]](#page-9-0), view-dependent appearance [\[25,](#page-9-1) [43\]](#page-9-2), and motion [\[26\]](#page-9-3). In particular, neural radiance fields (NeRFs) [\[25\]](#page-9-1), have been successfully used in problems such as novel view synthesis [\[3,](#page-8-2) [4,](#page-8-3) [67\]](#page-10-1), scene geometry extraction [\[56,](#page-10-2) [62\]](#page-10-3), capturing dynamic scenes [\[20,](#page-8-4) [30,](#page-9-4) [31,](#page-9-5) [34,](#page-9-6) [53\]](#page-10-4), 3D semantic segmentation [\[54,](#page-10-5) [69\]](#page-10-6), and robotics [\[1,](#page-8-5) [13,](#page-8-6) [22\]](#page-8-7). + +Despite the progress, it remains challenging to build neural fields that represent an entire category of objects. Previous methods sidestep the problem by overfitting on a single instance [\[25\]](#page-9-1), or learning [\[23,](#page-8-1) [29,](#page-9-0) [64\]](#page-10-7) on datasets like ShapeNet [\[5\]](#page-8-8) that contain objects that are manually canonicalized – oriented consistently for 3D position and orientation (3D pose) across a category. This strong supervision makes it easier to learn over categories, but limits their application to data that contain these labels. Recent work has proposed methods for self-supervised learning of 3D pose canonicalization [\[41,](#page-9-7) [44,](#page-9-8) [48\]](#page-10-8), however, these operate on 3D point clouds, meshes, or voxels – but not neural fields. + +In this paper, we present Canonical Field Network (CaFi-Net), a self-supervised method for category-level canonicalization of the 3D position and orientation of objects represented as neural fields, specifically neural radiance fields. Canonicalizing neural fields is challenging because, unlike 3D point clouds or meshes, neural fields are continuous, noisy, and hard to manipulate since they are parameterized as the weights of a neural network [\[61\]](#page-10-9). To address these challenges, we first extend the notion of equivariance to continuous vector fields and show how networks for processing 3D point clouds [\[51\]](#page-10-10) can be extended to operate directly on neural radiance fields. We design CaFi-Net as a Siamese network that contains layers to extract equivariant features directly on vector fields. These field features are used to learn a canonical frame that is consistent across instances in the category. During inference, our method takes as input neural radiance fields of object instances from a category at arbitrary pose and estimates a canonical field that is consistent across the category. To handle noise in radiance fields from NeRF, our method incorporates density-based feature weighting and foregroundbackground clustering. + +Our approach learns canonicalization without any supervision labels on a new dataset of 1300 pre-trained NeRF models of 13 common ShapeNet categories in arbitrary 3D pose (see Figure [1\)](#page-0-0). We introduce several self-supervision loss functions that encourage the estimation of a consistent canonical pose. In addition, we present extensive quantitative comparisons with baselines and other methods on standardized canonicalization metrics [\[38\]](#page-9-9) over 13 object categories. In particular, we show that our approach matches or exceeds the performance of 3D point cloud-based methods. This enables the new capability of directly operating on neural fields rather than converting them to point clouds for canonicalization. To sum up, we contribute: + +- Canonical Field Network (CaFi-Net), the first method for self-supervised canonicalization of the 3D position and orientation (pose) of objects represented as neural radiance fields. +- A Siamese neural network architecture with equivariant feature extraction layers that are designed to directly operate on continuous and noisy radiance fields from NeRF. +- A public dataset of 1300 NeRF models from 13 ShapeNet categories including posed images, and weights for evaluating canonicalization performance. + +# Method + +Our method assumes that we have pre-trained NeRF models of scenes containing single object instances at arbitrary poses from common shape categories. Our scenes contain primarily the object, but also some background – perfect foreground-background segmentation is not available. Our goal is to build a method that estimates a canonicalizing SE(3) transformation that aligns these object NeRFs to a consistent category-level canonical field. + +Rather than operate on 3D point clouds, meshes, or voxels, our method (see Figure [3\)](#page-3-0) operates directly on samples from the continuous neural radiance field. During training, we learn canonical fields from a pre-trained NeRF dataset in a fully self-supervised manner. We use a Siamese network architecture that extracts equivariant field features for canonicalization (see Figure [3\)](#page-3-0). During inference, given a previously unseen NeRF model of a new object instance in an arbitrary pose, our method estimates the transformation that maps it to the canonical field. + +For a pre-trained neural radiance field that maps 3D position (x, y, z) and direction θ, ϕ to color and density (c, σ), the input to our method consists of samples on the density field. We do not use color because it is direction-dependent in NeRF and also varies for different instances. + +Preprocessing: During both training and inference, we uniformly sample the pre-trained NeRF density field to find the object center and its bounding box (see supplementary document for details). We then sample a uniform grid in the density field of the object's bounding box. We empirically choose uniform grid sampling over fully random sampling since we found no difference. The uniformly sampled density grid is then normalized to obtain density values σD in the continuous range [0, 1], *i.e*., + +$$\boldsymbol{\sigma}_D := \{1 - exp(-d \cdot \Gamma_r^{\sigma}(x)) \mid x \in \boldsymbol{X}\}, \tag{1}$$ + +![](_page_3_Figure_0.jpeg) + +Figure 3. **CaFi-Net** samples a density field from NeRF and uses its **density**, **position** and **density gradients** as input signals (A) to canonicalize the field. We predict rotation equivariant features and weigh them by density (B) to guide our learning from the occupied regions of the scene. We then compute an invariant embedding by taking a dot product between equivariant features. This invariant embedding is used to canonicalize the field that enables rendering all the objects in the canonical frame (E). Our method also applies an inter-instance consistency loss (D) that aligns different instances of the same category in the canonical frame. **We do not assume pre-canonicalized fields, and canonicalize in a self-supervised manner**. + +where X is the regularly sampled grid within the object bounding box and d is the depth step size used in the coarse NeRF sampling. **Translation** and **scale** canonicalization is simply achieved by moving the bounding box's center [27] and scaling it, so our next concern is on rotation canonicalization. + +CaFi-Net (Canonical Field Network) is our method for 3D rotation canonicalization using NeRF's density field (red box in Figure 3). We now describe its components. + +**Signal Representation**: At each of the sampled density field locations, we have several choices of signals to use as the input for CaFi-Net. For instance, we can (1) use the NeRF density value directly, (2) use the xyz location of the sampled field, or (3) use the gradient of the density field. Densities alone do not capture the position and directional components of the field, so we use a combination of densities and gradients of densities at query locations of the grid as our input signal for CaFi-Net. Gradients of density capture the object surface and help in canonicalization (see ablation study in Section 5). + +**Equivariant Convolution**: To canonicalize for rotation, we leverage equivariance properties of spherical harmonic functions to obtain rotation-equivariant learnable features on the field [35, 51]. Type- $\ell$ spherical harmonic functions $(Y^{\ell})$ are degree- $\ell$ polynomials of points on the sphere that + +are equivariant to $SO(2\ell+1)$ . The densities sampled from NeRF reconstructions are often noisy and contain outliers. To promote our method to learn only from occupied regions, we weigh each equivariant feature type by its density. This weighing has no effect on the equivariance of features (see supplementary document for proof). We have the option to weigh equivariant features with the local average of the density to smoothen the signal or to weigh directly by the density. We found weighing by density worked better than taking the local average density (see Section 5). We learn equivariant features of type $\ell$ as: + +$${}^{R}s^{\ell} := f_w({}^{R}x) \left( W^{\ell}(\mathsf{EQConv}^{\ell}(\nabla(\boldsymbol{\sigma}_D({}^{R}x)))) + \delta_{0\ell}b \right), \quad (2)$$ + +$$f_w(^R x) := \begin{cases} \operatorname{mean}_r(\boldsymbol{\sigma}_D(^R x)), & or \\ \boldsymbol{\sigma}_D(^R x), \end{cases}$$ +(3) + +where, $W^\ell$ and b are the weights and biases, ${}^Rx$ is a 3D point queried at resolution R and $\operatorname{mean}_r(\sigma_D({}^Rx))$ is the average density at location ${}^Rx$ from points sampled at radius r. We hierarchically aggregate features at resolutions $\frac{1}{2}, \frac{1}{4}$ , and $\frac{1}{8}$ to obtain global equivariant features. The EQConv $^\ell$ convolution layer is the same as defined in Section 3. We employ non-linearities from [35] that preserve equivariance and capture better equivariant features. We also compute the max-pool of point-wise features of the last layer to obtain global type- $\ell$ equivariant features $F^\ell$ . + +**Canonicalization**: After obtaining the global type- $\ell$ + +equivariant feature $F^\ell$ , we compute its dot product with point-wise spherical harmonics $Z^\ell$ scaled by their norms $Z^\ell(X) = ||X||_2 Y^\ell(X/||X||_2)$ to obtain a pose-invariant embedding $H^\ell$ [38]. As the two vectors $F^\ell$ and $Z^\ell$ are both equivariant to input rotation, their dot product is invariant. We use a linear layer on top of the pose-invariant embedding to estimate grid coordinates in the canonical frame $(P \in \mathbb{R}^{H \times W \times D \times 3})$ for each point in X and M equivariant transformations $E \in \mathbb{R}^{M \times 3 \times 3}$ [38] that orient the canonical coordinates to the input coordinates. We then choose the best canonicalizing transform $E_b$ and penalize it for canonicalization. The invariant embedding H, grid coordinates P and equivariant rotation E are given as, + +$$H^{\ell}(\boldsymbol{\sigma}_{D}, \boldsymbol{X}) := \langle F^{\ell}(\boldsymbol{\sigma}_{D}), Z^{\ell}(\boldsymbol{X}) \rangle, \tag{4}$$ + +$$P := MLP_Y(H(\boldsymbol{\sigma}_D, \boldsymbol{X})), \tag{5}$$ + +$$E := MLP_E(F(\boldsymbol{\sigma}_D, \boldsymbol{X})). \tag{6}$$ + +Here, we have dropped the type- $\ell$ notation for H and F as we concatenate all equivariant feature types. + +**Siamese Network Architecture**: Experimentally, canonicalizing noisy density fields is difficult without guidance on shape similarity within a category. We therefore use a Siamese training strategy [27] where two different object instances are forced to be consistently canonicalized (see the lower branch in Figure 3). + +**Clustering**: CaFi-Net predicts a canonical coordinate for every grid point, but unlike point clouds, fields have many unoccupied regions that do not provide any information. We penalize the network on foreground regions only by performing K-means clustering on the densities within the object bounding box with K=2 and choose the cluster $C_f$ with a higher mean. Note that we still operate on continuous fields, but only cluster samples to guide our training. + +CaFi-Net is trained on a large dataset of pre-trained NeRF models over 13 object categories (see Section 5). We use the following loss functions to train our model. + +Canonicalization Loss: To self-supervise our learning, we transform the predicted canonical grid coordinates to the input grid using the canonical rotation $E_b$ and compute a point-wise L2 distance between them. This loss forces the predicted invariant grid coordinates P to reconstruct the shape and forces the predicted canonicalizing transform to be equivariant to the input transformation. The final loss is computed over the canonicalizing transform that minimizes this loss: + +$$E_b := \min_{j} \left( \operatorname{mean}_{i \in C_f} || \boldsymbol{X}_i - E_j P_i ||_2^2 \right), \tag{7}$$ + +$$\mathcal{L}_{canon} := \operatorname{mean}_{i \in C_f} || \boldsymbol{X}_i - E_b P_i ||_2^2,$$ + (8) + +where $C_f$ is a set of points belonging to the foreground. + +**Orthonormality Loss**: The predicted equivariant transformations E should have orthonormal vectors and we use the following to force orthonormality using: + +$$U_i, D_i, V_i := SVD(E_i), \tag{9}$$ + +$$\mathcal{L}_{ortho} := \frac{1}{M} \sum_{j} \left| \left| E_{j} - U_{j} V_{j}^{T} \right| \right|_{2}, \tag{10}$$ + +where $SVD(E_j)$ computes the singular value decomposition of the $j^{th}$ equivariant transformation. + +Siamese Shape Loss: We use a Siamese loss that penalizes for consistency between different shapes belonging to the same category. Given two un-canonicalized fields in different frames of reference $\sigma_D^1$ and $\sigma_D^2$ , we canonicalize both the fields to the canonical frame and compute chamfer distance between their predicted canonical coordinates for points in the foreground cluster $C_f$ , i.e., + +$$\mathcal{L}_{siamese} := CD(P(\sigma_D^1), P(\sigma_D^2)). \tag{11}$$ + +This loss regularizes the training by ensuring that instances of the same category should be aligned in the canonical frame. + +**Architecture and Hyper-parameters**: CaFi-Net predicts a 128 dimensional invariant embedding for each point in space and performs convolution at resolutions 1/2, 1/4, and 1/8. Each equivariant convolution layer aggregates features from 512 neighboring points at twice the resolution. We then use equivariant non-linearities introduced in [35] by applying Batch Normalization [14] and ReLU activation [2] in the inverse spherical harmonic transform domain. We use a three-layer MLP with intermediate Batch Normalization and ReLU layers followed by a final linear layer to predict canonical coordinates. To train our network, we weigh the canonicalization loss ( $\mathcal{L}_{canon}$ ) the highest with weight 2.0 and 1.0 for all the other loss functions. All our models are written in PyTorch [32] and are trained for 300 epochs with a batch size of 2 on an Nvidia 1080-Ti GPU. We use the Adam Optimizer [17] with an initial learning rate of 6e-4and L2 weight regularization of 1e-5 for all our experiments. Please see the supplementary document for more details. diff --git a/2212.09013/main_diagram/main_diagram.drawio b/2212.09013/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e5e654355519d75f93bba02e353d074df772b6f0 --- /dev/null +++ b/2212.09013/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2212.09013/main_diagram/main_diagram.pdf b/2212.09013/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5afbbd2e700111769c23825d05c29856409deb12 Binary files /dev/null and b/2212.09013/main_diagram/main_diagram.pdf differ diff --git a/2212.09013/paper_text/intro_method.md b/2212.09013/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..a4f5e2f13c49206ea9d22a8658aacef9bef88fa7 --- /dev/null +++ b/2212.09013/paper_text/intro_method.md @@ -0,0 +1,102 @@ +# Introduction + +Human Action Recognition (HAR) methods based on skeleton data have been widely investigated and received considerable attention due to high accuracy achieved on benchmark datasets such as NTU RGB+D [@shahroudy2016ntu] and Kinetics [@carreira2017quo]. Recently most of the methods that achieve the state-of-the-art (SOTA) accuracy on benchmark datasets have been based on graph convolutional network (GCN) deep learning methods [@yan2018spatial; @shi2019two; @shi2020skeleton]. This accuracy increment can be attributed to many factors such as view invariance, robustness to occlusion and segmentation of the skeleton. + +Spatial Temporal Graph Convolutional Network (ST-GCN) [@yan2018spatial] architecture was the first to utilize graph neural networks for skeleton-based human activity recognition and had achieved the SOTA results outperforming CNN and LSTM based models [@yan2018spatial; @peng2021stgcnnew]. Furthermore, it has been used with small and distinct datasets in different scenarios including fall detection [@Keskes2021VisionBasedFD; @zheng2019fall], hand sign detection [@amorim2019; @li2019hand] and others [@10.1145/3341162.3345581; @tsai2021]. + +Child activity recognition (CAR) has important applications in video game development [@10.1145/3424636.3426909], early detection of autism [@zunino2018video; @zhang_application_2021], safety monitoring [@goto2013], object-play behavior assessment [@Westeyn2012] and many others. As most of the HAR models are based on adult datasets and as previous studies in motion style transfer [@10.1145/3424636.3426909] and pose estimation [@sciortino_estimation_2017] have shown that due to differences in size, anatomy, and motion, these models can't be used for CAR, it is required to develop robust and well generalized CAR models. + +Early CAR models as well as small dataset based CAR models have utilized signal processing methods along with classical machine learning methods [@rehg2013decoding; @tsiami2018]. Deep Learning (DL) methods have been used lately with image based, skeleton based and wearable sensor based approaches through CNN [@huang2015human; @silva2021skeleton], LSTM [@dechemi2021; @Hadfield2018] and other methods [@suzuki2012activity; @efthymiou2018multi]. As GCN based models have not been applied in the past on CAR, we direct this research to fill that gap. + +Though there are claims made on the availability of public datasets for CAR [@lemaignan_pinsoro_2018; @sun2019; @Rajagopalan2013; @marinoiu2018], few of those were not accessible due to non-availability and few were not capturing the whole body. Most of the existing DL research on CAR have not used the limited public datasets but has demonstrated the results on private datasets. Addressing this research gap, we have considered two standard Kinect camera based child activity datasets named Kinder-gator [@Aloba2018kindergator], Child-Whole-Body-Gesture (CWBG) [@Vatavu2019CFBG] and a standard motion capture system based dataset named kinder-gator 2.0 [@dong_yuzhu_2020_4079507] and we present the first evaluation of CWBG dataset on a SOTA DL model. + +Though transfer learning (TL) can be used to overcome the limited dataset challenge in public datasets, [@efthymiou2018multi; @suzuki2012activity; @sciortino_estimation_2017] suggest that TL from adult to child is a challenging task resulting in undesirable performance. While there is a TL implementation with ST-GCN architecture [@Keskes2021VisionBasedFD] in HAR, it lacks a detailed study of different TL approaches and TL performance on ST-GCN architecture. + +Based on the literature study and the gaps identified, in this paper we provide the following main contributions, + +- To the best of our knowledge, this is the first implementation of a GCN based model for child activity recognition. + +- We provide the first benchmark results on a publicly available child activity dataset using ST-GCN model. + +- Our research shows that comparable results can be achieved through transfer learning with ST-GCN model and demonstrate performance and comparative analysis with different learning approaches. + +- A pre-train dataset selection method is introduced in this research to improve transfer learning and reduce negative transfer learning based on curriculum learning concept. + +The rest of the paper is organized as follows. The models and methods used in the ST-GCN based implementations are discussed in Section [2](#section:methodology){reference-type="ref" reference="section:methodology"}. Pre-processing process used with each dataset and the experimental setups used in each case are discussed in Section [3](#section:experiments){reference-type="ref" reference="section:experiments"}. The performance of learning methods and the best results achieved by each method on different protocols are discussed in Section [4](#section:results,discussion){reference-type="ref" reference="section:results,discussion"} while Section [5](#section:conclusion){reference-type="ref" reference="section:conclusion"} concludes with future research directions. + +# Method + +Based on the ST-GCN original paper [@yan2018spatial] and on the official ST-GCN PyTorch implementation [@mmskeleton2019], a TensorFlow based model was implemented and further experiments were carried out based on this model with NTU RGB+D dataset used for the pre-training models. + +Two approaches were applied to quantitatively evaluate the ST-GCN model on the available child activity datasets: + +- Standard Deep Learning: Train the ST-GCN model on the child activity datasets directly and do the hyperparameter tuning to attain a suitable model, + +- Transfer Learning: Use a pre-trained ST-GCN model to leverage the learnt feature representations. + +
+ +
ST-GCN model and feature extraction pipeline
+
+ +Under this approach, ST-GCN model was directly trained with child activity datasets. Due to the small size of these datasets, the original ST-GCN model with 256 channels in each of 10 ST-GCN layers could appear to contain excessive capacity. Since excessive capacity result in over-fitting [@wang2016machine], model tuning was also attempted with different number of filters as detailed in Section [4.1](#subsection: SDL){reference-type="ref" reference="subsection: SDL"}. + +Lack of acceptable scale datasets generally results in poor performance of deep learning models. As all the openly available child activity datasets are small in size, developing improved learning methods was essential. Several TL approaches such as fine tuning [@2018; @5288526; @tan2018survey], and feature extraction [@zhuang2020comprehensive; @zhang2019transfer; @sharif2014cnn_feature_extraction] have been used in the subsequent implementations to improve the performance of the model. + +Fine tuning of the pre-trained ST-GCN model was done using several methods as detailed in TL literature [@yosinski2014transferable; @zhang2019recent]. + +1. Frozen layer approach - Fine tuning $n$ top ST-GCN layers, where $1\leq n < 10$. + +2. Hybrid approach - In the original ST-GCN model in Fig. [1](#fig:STGCN_model){reference-type="ref" reference="fig:STGCN_model"}, $n$ top ST-GCN layers were randomly initialized where $1\leq n \leq 10$. + + - Hybrid-Frozen : Combines feature extraction and standard deep learning together. Bottom $10-n$ ST-GCN layers were kept frozen. + + - Hybrid-FineTuned : Combines fine tuning and standard deep learning together. Bottom $10-n$ ST-GCN layers were fine tuned. + +3. Propagation approach - Fine tuning all ST-GCN layers. + +Since ST-GCN contains only a single layer classifier, initial experiments were done with a single randomly initialized dense layer. Experiments were later done with the FC classifiers with up to 4 dense layers. A dropout layer was used as a regularization method when overfitting occurred. Hyper-parameter tuning was also applied to get the best accuracy. + +Inspired by [@awais_can_2020; @yu_feature_extract_stratified_2017], the feature representations were extracted from feature maps of the ST-GCN model (Fig. [1](#fig:STGCN_model){reference-type="ref" reference="fig:STGCN_model"}). The original ST-GCN model was then supplemented with either a flattening layer or a global average pooling (GAP) layer as the intermediate layer between the ST-GCN model and the classifier. Fusion of feature maps was done with consecutive maps from two and three layers of ST-GCN. Enhancing this approach further, dimensionality reduction techniques such as principle component analysis (PCA), truncatedSVD were employed. Experiments were done with support vector machine (SVM), logistic regression as well as feed forward neural network (FFNN) as the classifier. + +To improve the efficiency of both Standard Deep Learning and TL, Curriculum Learning (CL) [@bengio2009curriculum] was applied, where the model was trained starting from easy samples and gradually exposing to more challenging samples. While there are several variants in implementing CL, our implementation contained Scoring and Pacing functions as in [@hacohen2019power]: + +- Scoring Function: gives the probability of level of difficulty of each sample, calculated by training the model with 10% of epochs. + +- Pacing Function: decides the number of samples used in each epoch enabling us to add the samples based on the descending order of the values generated by the Scoring Function. This was implemented as a step function with varying number of steps. + +A preliminary research was done on the existing child activity datasets and found that all three publicly available datasets, Kinder-Gator [@Aloba2018kindergator], Kinder-Gator 2.0 [@DongYuzhu2020kindergator] and CWBG dataset [@Vatavu2019CFBG] were depth sensor based and were available only in skeleton modality. In order to overcome the challenge in limited data sets, we used existing adult skeleton mode activity data sets, Kinect v2 based NTU-120 and NTU-60 for building the pre-trained models. NTU-120 was taken as the main dataset, which contains 850 videos with an average number of frames ranging from 76 to 300. Actions are performed by 106 participants above 10 years of age. + +To improve the TL and to reduce the negative TL effect [@wang2019characterizing], several subsets of NTU were identified. + +- NTU-120: The full NTU RGB+D-120 dataset was used with a different dataset splitting method as detailed in Section [3.1](#SubSec:Data Pre){reference-type="ref" reference="SubSec:Data Pre"} than the one proposed in [@2020ntu120]. With this approach, we were able to remove any potential bias resulting from an unbalanced data distribution. + +- NTU-60: We used the full NTU RGB+D dataset along with 11 interaction classes. + +- NTU-51: Out of the datasets of NTU-60, in order to minimize ambiguities in activity identification, 9 interaction classes were removed resulting in 49 single action classes and 2 interaction classes. + +While there are other methods [@minmax_minghui2019; @tute_sibgrapi_2017], we used a simplified CL inspired approach to select better classes. Best classes are chosen by analysing the confusion matrices of NTU-60/120 based STGCN models. + +- NTU-44: Out of those sorted, 44 classes were selected. To reduce ambiguities introduced from spatial and temporal symmetrical classes, we kept only one such class in this subset. + +- NTU-22: Enhancing the approach taken for NTU-44 further, a more discriminative dataset of 22 classes was introduced. Classes were selected by analysing the NTU-44 confusion matrix. + +Since the CWBG dataset is recorded with an approximate 10FPS frame rate, we introduced three down-sampled data sets. + +- NTU-44-FRA : Selected each of the 3rd frame in every sequence in NTU-44 subset. + +- NTU-60-FRA : Selected each of the 3rd frame in every sequence in NTU-60 subset. + +- NTU-120-FRA : Selected each of the 3rd frame in every sequence in NTU-120 subset. + +As the NTU dataset was created with Kinect v2 and CWBG dataset was created using Kinect v1, output skeleton structure generated in NTU and CWDG are different to each other. + +Transfer learning when applied to convolution based models usually takes a input as a grid structure similar to images. Thus, when the target domain data samples differ in size from source domain, resizing with standard interpolation techniques can be utilized. But with graph structure, structural change has to be taken into consideration and knowledge transferability in GCN based models is still not fully developed [@zhu2021transfer]. Thus we use 20 shared joints with removal of 5 joints from NTU source dataset. + +The CWBG dataset is released for the study of child gesture elicitation and contains 1312 sequences from 30 children between the ages of 3 - 6 with an equal gender distribution. + +- CWDG-Full: Contains 15 classes and is used to test the performance of entire dataset. + +- CWDG-Similar: Contains 10 classes, removing most dissimilar classes. This group contains challenging classes even for a human (Table [1](#CWBG classes){reference-type="ref" reference="CWBG classes"} - column Challenging). + +- CWDG-Dissimilar: Contains 10 classes including most dicriminative (Table [1](#CWBG classes){reference-type="ref" reference="CWBG classes"} - column Discriminative). diff --git a/2304.08809/main_diagram/main_diagram.drawio b/2304.08809/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..6cfb0470201f234da2d9c293fa00758b8592581c --- /dev/null +++ b/2304.08809/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2304.08809/main_diagram/main_diagram.pdf b/2304.08809/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..31b8ff2e17410709b3a3a1b767d6d1f807e7100b Binary files /dev/null and b/2304.08809/main_diagram/main_diagram.pdf differ diff --git a/2304.08809/paper_text/intro_method.md b/2304.08809/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..20414592b5a4683f432c0c93d221cc6fa2ebbd5a --- /dev/null +++ b/2304.08809/paper_text/intro_method.md @@ -0,0 +1,277 @@ +# Introduction + +With the rapid development of deep neural networks for computer vision and natural language processing, there has been growing interest in learning correspondences across the visual and text modalities. A variety of vision-language pretraining frameworks have been proposed [\[12,](#page-8-0) [25,](#page-8-1) [32,](#page-9-0) [38\]](#page-9-1) for learning high-quality cross-modal representations with weak supervision. Recently, progress on visual transformers (ViT) [\[5,](#page-8-2)[16,](#page-8-3)[35\]](#page-9-2) has enabled seamless integration of both modalities into a unified attention model, leading to imagetext transformer architectures that achieve state-the-art performance on vision-language benchmarks [\[1,](#page-8-4) [30,](#page-9-3) [51\]](#page-10-0). + +Progress has also occurred in *video*-language pretraining by leveraging image-text models for improved frame-based + +![](_page_0_Picture_10.jpeg) + +![](_page_0_Figure_11.jpeg) + +Figure 1. We propose **SViTT**, a *sparse* video-text transformer for efficient modeling of temporal relationships across video frames. Top: Semantic information for video-text reasoning is highly localized in the spatiotemporal volume, making dense modeling inefficient and prone to contextual noises. Bottom: **SViTT** pursues *edge* sparsity by limiting query-key pairs in self-attention, and *node* sparsity by pruning redundant tokens from visual sequence. + +reasoning [\[4,](#page-8-5) [9,](#page-8-6) [18\]](#page-8-7). Spatial modeling has the advantage of efficient (linear) scaling to long duration videos. Perhaps due to this, single-frame models have proven surprisingly effective at video-text tasks, matching or exceeding prior arts with complex temporal components [\[9,](#page-8-6)[27\]](#page-9-4). However, spatial modeling creates a bias towards static appearance and overlooks the importance of temporal reasoning in videos. This suggests the question: Are temporal dynamics not worth modeling in the video-language domain? + +Upon a closer investigation, we identify a few key challenges to incorporating multi-frame reasoning in videolanguage models. First, limited model size implies a tradeoff between spatial and temporal learning (a classic example being 2D/3D convolutions in video CNNs [\[53\]](#page-10-1)). For any given dataset, optimal performance requires a careful bal- + +\*Work done during an internship at Intel Labs. + +ance between the two. Second, long-term video models typically have larger model sizes and are more prone to overfitting. Hence, for longer term video models, it becomes more important to carefully allocate parameters and control model growth. Finally, even if extending the clip length improves the results, it is subject to diminishing returns since the amount of information provided by a video clip does not grow linearly with its sampling rate. If the model size is not controlled, the computational increase may not justify the gains in accuracy. This is critical for transformerbased architectures, since self-attention mechanisms have a quadratic memory and time cost with respect to input length. In summary, model complexity should be adjusted adaptively, depending on the input videos, to achieve the best trade-off between spatial representation, temporal representation, overfitting potential, and complexity. Since existing video-text models lack this ability, they either attain a suboptimal balance between spatial and temporal modeling, or do not learn meaningful temporal representations at all. + +Motivated by these findings, we argue that video-text models should learn to allocate modeling resources to the video data. We hypothesize that, rather than uniformly extending the model to longer clips, the allocation of these resources to the relevant spatiotemporal locations of the video is crucial for efficient learning from long clips. For transformer models, this allocation is naturally performed by pruning redundant attention connections. We then propose to accomplish these goals by exploring transformer sparsification techniques. This motivates the introduction of a *Sparse Video-Text Transformer* (**SViTT**) inspired by graph models. As illustrated in Fig. [1,](#page-0-0) **SViTT** treats video tokens as graph vertices, and self-attention patterns as edges that connect them. We design **SViTT** to pursue sparsity for both: *edge* sparsity aims at reducing query-key pairs in attention module while maintaining its global reasoning capability; *node* sparsity reduces to identifying informative tokens (e.g., corresponding to moving objects or person in the foreground) and pruning background feature embeddings. To address the diminishing returns for longer input clips, we propose to train **SViTT** with *temporal sparse expansion*, a curriculum learning strategy that increases clip length and model sparsity, in sync, at each training stage. + +**SViTT** is evaluated on diverse video-text benchmarks from video retrieval to question answering, comparing to prior arts and our own dense modeling baselines. First, we perform a series of ablation studies to understand the benefit of sparse modeling in transformers. Interestingly, we find that both nodes (tokens) and edges (attention) can be pruned drastically at inference, with a small impact on test performance. In fact, token selection using cross-modal attention improves retrieval results by 1% without re-training. + +We next perform full pre-training with the sparse models and evaluate their downstream performance. We observe that **SViTT** scales well to longer input clips where the accuracy of dense transformers drops due to optimization difficulties. On all video-text benchmarks, **SViTT** reports comparable or better performance than their dense counterparts with lower computational cost, outperforming prior arts including those trained with additional image-text corpora. + +The key contributions of this work are: 1) a video-text architecture **SViTT** that unifies edge and node sparsity; 2) a sparse expansion curriculum for training **SViTT** on long video clips; and 3) empirical results that demonstrate its temporal modeling efficacy on video-language tasks. + +# Method + +**Sparse configurations.** The sparsity of **SViTT** is controlled by the following hyperparameters: + +- Visual token keep rate $q_v^{(l)}$ and multimodal token keep rate $q_m^{(l)}$ per layer l for *node* sparsity; +- Local attention blocks $K_l$ , random attention blocks $K_r$ and block size G shared across layers for edge sparsity. + +Tab. 7 lists the configurations for each stage of pretraining and the corresponding sparsity s, computed as the percent of reduction in edges of sparsified attention graph $\mathcal G$ from that of a dense transformer. For the $l^{\text{th}}$ layer of visual encoder $f_v$ , the number of edges is given by + +$$|\mathcal{E}_v^{(l)}| = N_v^{(l)}(K_l + K_r)G$$ + (17) + +where input length $N_v^{(l)} = \lceil q_v^{(l-1)} N_v^{(l-1)} \rceil$ . For multimodal layers $f_m$ , the edge count is + +$$|\mathcal{E}_{m}^{(l)}| = N_{m}^{(l)} N_{t} \tag{18}$$ + +where $N_t$ denotes text length and $N_m^{(l)} = \lceil q_m^{(l-1)} N_m^{(l-1)} \rceil$ . Therefore an **SViTT** model with $L_v = 12$ visual layers and $L_m = 3$ multimodal layers has overall edge sparsity + + +$$S(q_v, q_m, K_l, K_r) = 1 - \frac{\sum_{l=1}^{L_v} |\mathcal{E}_v^{(l)}| + \sum_{l=1}^{L_m} |\mathcal{E}_m^{(l)}|}{L_v N_v^2 + L_m N_t N_v}$$ +(19) + +**Temporal expansion.** Transformer architectures do not require fixed input lengths as its operations are either pointwise (e.g. FFN) or permutation equivariant (e.g. MHSA). This makes the temporal expansion (Sect. 4) of input clips a mostly trivial process, except for the position embeddings, which does depend on spatiotemporal dimensions of inputs. + + + +| Frames T | Attn. blocks $K_l, K_r, G$ | Keep rate $q_v, q_m$ | Edges (M) $ \mathcal{E} $ | Sparsity S | +|----------|----------------------------|----------------------|---------------------------|------------| +| 4 | | (0.7, 0.1) | 1.48 | 0.80 | +| 8 | (1, 3, 56) | (0.6, 0.1) | 2.60 | 0.91 | +| 16 | | (0.5, 0.1) | 4.61 | 0.96 | + +Table 7. **SVITT Configurations.** We report hyperparameters controlling the edge and node sparsity for different clip lengths T, as well as the overall sparsity as computed by Eq. (19). + +Following prior work on training video transformers with image models, we *inflate* the 2D positional embedding + +$$\mathbf{P} = [\mathbf{p}_{\text{cls}}, \mathbf{p}_{1,1}, \dots, \mathbf{p}_{H,W}] \in \mathbb{R}^{(HW+1) \times d}$$ + (20) + +into a 3D embedding tensor + +$$\mathbf{P}' = [\mathbf{p}_{cls}, \mathbf{p}'_{1,1,1}, \dots, \mathbf{p}'_{T,H,W}] \in \mathbb{R}^{(THW+1) \times d}$$ + (21) + +for inputs of T frames, by duplicating the local embeddings $\mathbf{p}_{hw}$ along the temporal dimension: + +$$\mathbf{p}'_{t,h,w} = \mathbf{p}_{h,w}, \qquad \forall t, h, w \tag{22}$$ + +Likewise, expansion of clip length from $T_1$ to $T_2$ can be performed by temporally resizing the positional embedding, e.g. through nearest neighbors interpolation: + +$$\mathbf{p}'_{t,h,w} = \mathbf{p}_{\lfloor t \cdot \frac{T_1}{T_2} + \frac{1}{2} \rfloor, h, w}, \qquad \forall t, h, w$$ + (23) + +The BEiT backbone of visual encoder uses relative position bias [46] in every self-attention layer, which encodes a scalar added to each entry of the similarity matrix depending on the relative position between query and key patches: + +$$\mathcal{A}(\mathbf{Q}, \mathbf{K}, \mathbf{V}) = \sigma(\mathbf{Q}\mathbf{K}^T + \mathbf{B})\mathbf{V},\tag{24}$$ + +$$\mathbf{B}_{(h,w),(h',w')} = \mathbf{R}_{h'-h,w'-w} \tag{25}$$ + +where $\mathbf{R} \in \mathbb{R}^{(2H-1)\times(2W-1)}$ are learnable parameters. When expanding the input to multi-frame clips, we again inflate the relative position bias to the temporal dimension: + +$$\mathbf{B}'_{(t,h,w),(t',h',w')} = \mathbf{R}'_{t'-t,h'-h,w'-w},\tag{26}$$ + +$$\mathbf{R}' \in \mathbb{R}^{(2T-1)\times(2H-1)\times(2W-1)} \tag{27}$$ + + $\mathbf{R}'$ is initialized by interpolating $\mathbf{R}$ temporally, identical to the procedure for absolute positional embedding $\mathbf{P}'$ . + +**Pre-training. SViTT** is pre-trained on WebVid-2M [4] with 2.5 million video-text pairs scraped from the Internet. While alternative datasets exist for video-language pre-training such as HowTo100M [43] and YT-Temporal [63], we choose WebVid as it has higher caption quality, covers a wide range of scenes, and can be trained with a reasonable amount of resource. + + + +| Dataset | Avg. Dur. | # Videos | # Sent. / Q | +|---------------------|---------------|----------|-------------| +| Vide | eo-Text Pre-T | raining | | +| WebVid-2M [4] | 18s | 2.5M | 2.5M | +| Tex | t-to-Video Re | trieval | | +| MSR-VTT [58] | 15s | 10K | 200K | +| DiDeMo [2] | 28s | 10K | 40K | +| Charades [50] | 30s | 10K | 16K | +| SSv2-Label [27] | 4s | 171K | 112K | +| Video | Question Ar | iswering | | +| MSRVTT-QA [56] | 15s | 10K | 244K | +| ActivityNet-QA [61] | 180s | 5.8K | 58K | +| AGQA 2.0 [23] | 30s | 10K | 2.27M | + +Table 8. Pre-Training and Downstream Datasets. + +**Text-to-video retrieval.** We evaluate text-to-video retrieval on 4 datasets: MSR-VTT [58], DiDeMo [2], Charades [50] and Something-Something v2 [20]. MSR-VTT and DiDeMo are video-text datasets commonly used in prior work; Charades and SSv2 were initially collected for video action recognition, with an emphasis on human-object interactions and temporal modeling, but also includes text descriptions for each video clip. + +Video question answering. Video question answering is evaluated on MSRVTT-QA [56], ActivityNet-QA [61] and AGQA 2.0 [23], annotated on top of the videos from MSR-VTT [58], ActivityNet [10] and Charades [50] respectively. MSRVTT-QA consists of mostly descriptive questions which can be solved without intricate temporal reasoning. ActivityNet-QA focuses on human actions and spatiotemporal relation between objects, posing a greater challenge beyond frame-based reasoning. AGQA contains difficult questions involving the composition of actions, testing the generalization capacity of video-text models. + +Tab. 8 summarizes the statistics of all aforementioned datasets. + +**Pre-training tasks. SViTT** is pre-trained on three losses following prior art in VLP [12, 18, 27, 30, 31]. + +• Video-text contrastive (VTC) applies InfoNCE loss between the video embeddings $\mathbf{Z}_v$ and text embeddings $\mathbf{Z}_t$ extracted at [cls] locations of their respective encoder $f_v$ and $f_t$ : + +$$\mathcal{L}_{VTC} = \ell_c(\mathbf{Z}_v, \mathbf{Z}_t) + \ell_c(\mathbf{Z}_t, \mathbf{Z}_v), \qquad (28)$$ + +$$\ell_c(\mathbf{X}, \mathbf{Y}) = -\sum_{i=1}^{B} \log \frac{e^{\langle \mathbf{x}_i, \mathbf{y}_i \rangle / \tau}}{\sum_{j=1}^{B} e^{\langle \mathbf{x}_i, \mathbf{y}_j \rangle / \tau}}$$ +(29) + +&lt;sup>4Linear projection on top of $\mathbf{z}_v$ , $\mathbf{z}_t$ omitted. + + + +| Task | | Pre-training | | | Video-text Retrieval | | Video QA | +|--------------------------|------------|--------------|------------|------------|----------------------|------------|----------------| +| Frames T | 4 | 8 | 16 | 4 | 8 | 16 | 8 | +| Epochs | | 10 | | | 15 | | 5 (1 for AGQA) | +| Warm-up | | 1 | | | 0 | | 0 | +| Batch size | 512 | 336 | 192 | 64 | 48 | 32 | 128 | +| Learning rate | 3 × 10−5 | 1 × 10−5 | 5 × 10−6 | | 1 × 10−5 | | 5 × 10−5 | +| Weight decay | | 0.02 | | | 0.02 | | 0.02 | +| Text length | | 32 | | | 32 (64 for DiDeMo) | | 25 (Q), 5 (A) | +| Attn. blocks (Kl, Kr, G) | | (1, 3, 56) | | | (1, 3, 56) | | (1, 3, 56) | +| Keep rate (qv, qm) | (0.7, 0.1) | (0.6, 0.1) | (0.5, 0.1) | (0.7, 0.1) | (0.6, 0.1) | (0.5, 0.1) | (0.6, 0.5) | + +Table 9. Training Hyperparameters. + + + +| | | | | | | MSR-VTT | | | | DiDeMo | | +|------------------|------|--------|----------|------|------|---------|------|------|------|--------|------| +| Method | PT | Frames | Sparsity | R1 | R5 | R10 | Mean | R1 | R5 | R10 | Mean | +| VideoCLIP [57] | 100M | — | | 10.4 | 22.2 | 30.0 | 20.9 | 16.6 | 46.9 | — | — | +| Frozen [4] | 5M | 4 | | 23.2 | 44.6 | 56.6 | 41.5 | 21.1 | 46.0 | 56.2 | 41.1 | +| ALPRO [29] | 5M | 8 | — | 24.1 | 44.7 | 55.4 | 41.4 | 23.8 | 47.3 | 57.9 | 43.0 | +| VIOLET [18] | 5M | 4 | | 25.9 | 49.5 | 59.7 | 45.0 | 23.5 | 49.8 | 59.8 | 44.4 | +| Singularity [27] | 5M | 1 | | 28.4 | 50.2 | 59.5 | 46.0 | 36.9 | 61.1 | 69.3 | 55.8 | +| | | 1 | | 21.1 | 42.1 | 53.0 | 38.7 | 23.3 | 45.4 | 53.7 | 40.8 | +| Singularity* | 2M | 4 | — | 24.4 | 43.8 | 51.7 | 40.0 | 26.4 | 48.7 | 57.3 | 44.1 | +| | | 8 | | 24.3 | 44.5 | 54.3 | 41.0 | 25.8 | 50.0 | 60.7 | 45.5 | +| SViTT | | | Dense | 26.0 | 47.7 | 57.1 | 43.6 | 29.6 | 54.1 | 64.1 | 49.3 | +| | 2M | 8 | Hybrid | 25.4 | 48.4 | 57.5 | 43.8 | 31.0 | 57.2 | 66.3 | 51.5 | + +Table 10. Zero-shot Text-to-video Retrieval. Results reported in prior works marked in gray; \* indicates our reproduced results. + +• Video-text matching (VTM) learns a binary classifier on top of the [cls] output of multimodal encoder fm to discriminate between paired and misaligned videotext pair, optimized by binary cross entropy: + +$$\mathcal{L}_{VTM} = -\sum_{i=1}^{B} \left( \log(f_m(\mathbf{z}_{v,i}, \mathbf{z}_{t,i})) + \log(1 - f_m(\mathbf{z}_{v,i}, \mathbf{z}_{t,i'})) \right)$$ +(30) + +where i 0 6= i is a randomly selected negative sample. + +• Masked language modeling (MLM) requires the multimodal encoder fm to predict randomly masked out text tokens conditioned on the rest of text and video sequence, through a cross-entropy loss: + +$$\mathcal{L}_{\text{MLM}} = -\sum_{i=1}^{B} \sum_{j \in \mathcal{J}} [\mathbf{x}_t]_{i,j}^T \log \mathbf{y}_{i,j}$$ + (31) + +where [xt]i,j is a one-hot vector denoting the word at location j of example i, yi,j is the classifier output predicting the word at the same location, and J is the set of masked indices. + +We use equal weights for all three losses. + +Downstream tasks. We follow the downstream evaluation setup of Singularity [\[27\]](#page-9-4) for the most part. Text-tovideo retrieval is performed by ranking all candidate videos xv of the test set by their matching scores to text query xt. For video QA, a transformer decoder is applied on top of multimodal encoder fm to generate the answer. + +Training hyper-parameters. We use a sparse frame sampling strategy following [\[4,](#page-8-5) [18,](#page-8-7) [27\]](#page-9-4), splitting input videos into T chunks and randomly selecting one frame from each during training. Video frames are preprocessed with random resized cropping into spatial resolution of 224 × 224, resulting in 14 × 14 spatial patches. All models are optimized using AdamW [\[37\]](#page-9-22) (β1 = 0.9, β2 = 0.999) with a cosine learning rate schedule and warm-up training. We use 10 epochs for pre-training and 15 for fine-tuning on all datasets other than AGQA, which uses 1 epoch due to its large size. Batch size B and learning rate η are adjusted depending on memory costs of sparse models. Tab. [9](#page-12-1) summarizes the hyperparameters used for each task and model variant. + +We include full retrieval results with Recall@{1, 5, 10} in Tab. [10](#page-12-2) (zero-shot) and Tab. [11](#page-13-1) (fine-tuned). + + + +| M-4b-d | DT | E | C | | Cha | rades | | | SSv2- | Label | - | | +|-------------------------|--------------|--------|----------|--------|------|-------|------|------|-------|-------|------|------| +| Method | PT | Frames | Sparsity | R1 | R5 | R10 | Mean | R1 | R5 | R10 | Mean | | +| Frozen [4] | 5M | 32 | | 11.9 | 28.3 | 35.1 | 25.1 | | _ | | | | +| CLIP4Clip [39] | 400M | 12 | | 13.9 | 30.4 | 37.1 | 27.1 | 43.1 | 71.4 | 80.7 | 65.1 | | +| ECLIPSE [34] | 400M | 32 | | 15.7 | 32.9 | 42.4 | 30.3 | | _ | _ | | | +| MKTVR [40] | 400M | 42 | _ | 16.6 | 37.5 | 50.0 | 34.7 | | _ | _ | | | +| Cin audamiter [27] | 5M | 1 | | | | | | 36.4 | 64.9 | 75.4 | 58.9 | | +| Singularity [27] | 5M | 4 | | | | | | 44.1 | 73.5 | 82.2 | 66.6 | | +| C17: mm | 2M | Q | Dense | 16.0 | 34.9 | 47.2 | 32.7 | 43.6 | 72.6 | 82.2 | 66.1 | | +| SViTT | Z1 V1 | 8 | 8 Hybrid | Hybrid | 17.7 | 39.5 | 49.8 | 35.7 | 47.5 | 76.3 | 84.2 | 69.3 | + +Table 11. **Text-to-video Retrieval with Fine-tuning.** † denotes concurrent work. + + + +| Method | | РТ | F | DiDeMo | | | | | +|--------|-----------------|----|--------|--------------|--------------|------|--------------|--| +| | | PI | Frames | R1 | R5 | R10 | Mean | | +| Frozen | [4] | 5M | 4 | 21.1 | 46.0 | 56.2 | 41.1 | | +| SViTT | Dense
Hybrid | 2M | 8 | 21.9
22.9 | 45.6
47.7 | | 41.4
42.9 | | + +Table 12. Zero-shot Retrieval with SViTT on Frozen Baseline. + +![](_page_13_Figure_4.jpeg) + +Figure 7. **Node Sparsity for 4- and 8-frame Models.** Model of longer clip length is more robust to node sparsification. + +In addition to the Singularity baseline with BEiT-B backbone used in the main paper, we also evaluate ${\bf SViTT}$ on a simpler structure from Frozen [4]. This is also a two-tower model with separate video and text encoders $f_v, f_t$ , but unlike most vision-language transformers, does not contain a cross-modal encoder on top. Frozen is trained solely on the InfoNCE loss between video and text embeddings, and uses their cosine similarity to perform retrieval. While the cross-modal node sparsification does not apply to this framework, visual node sparsity and edge sparsity can still be applied to the visual encoder $f_v$ to enable temporal learning across frames. + +The original Frozen model uses a divided space-time attention similar to TimeSformer [8], where temporal attention is added to a pre-trained ViT and initialized as identity mapping. During early experiments, however, we find that the temporal module with zero-init fails to learn meaningful attention across frames, with query and key matrices stuck at zero weights. We opted to remove the temporal attention modules and make the spatial attention global instead (i.e. each token attends to every token from the video clip, + + + +| 0 | | MSI | R-VTT | | DiDeMo | | | | +|----------|------|------|-------|------|--------|------|------|------| +| Order | R1 | R5 | R10 | Mean | R1 | R5 | R10 | Mean | +| Standard | 21.0 | 43.0 | 51.5 | 38.5 | 29.1 | 53.5 | 63.1 | 48.6 | +| Morton | 20.6 | 40.6 | 49.4 | 36.9 | 27.3 | 51.9 | 61.9 | 47.1 | +| Hilbert | 20.3 | 40.9 | 49.6 | 36.9 | 27.9 | 52.5 | 62.5 | 47.7 | + +Table 13. **Ablation on Token Ordering.** We compare the standard **SViTT** trained with flattened video tokens and reordering using space-filling curves. + +instead of just those from the same frame). + +Tab. 12 shows the performance of **SViTT** applied to the Frozen model. Similar to the results in the main paper, our dense spatiotemporal transformer with the above modifications outperformed the original implementation of [4], despite being trained without image-text data (CC3M [49]). **SViTT** with hybrid sparsity again outperforms the dense version while using less computation and training memory. + +To demonstrate the claim that video sparsity increases with clip length, we evaluate dense models trained with clip length 4 and 8 under different levels of sparsity. As shown in Fig. 7, the 8-frame model is more robust to token pruning with lower keep rates. On DiDeMo, it outperforms 4-frame model by 4% at $q_v=0.5$ , while the two models differ by under 2% under dense evaluation. This reveals that longer clips contain greater level of redundancy, and should be modeled with higher sparsity (as done in this work). + +In edge sparsification, the flattened video sequence $\mathbf{z}_{1:N}$ is chunked into subsequences of length G. While this strategy is straightforward and common in language transformers [7,62], it breaks the spatiotemporal continuity of video data. We investigate an alternative to naïve chunking, by reordering the input tokens using space-filling curves such as Morton [44] and Hilbert [24] curves. This ensures that neighboring tokens in the flattened sequence are close to each other in the original multidimensional space, leading to more localized chunks. + +![](_page_14_Figure_1.jpeg) + +Figure 8. Qualitative Results. We visualize node sparsity patterns generated by visual (qv = 0.6) and cross-modal encoder (qm = 0.1). + +However, early experiments showed no benefit of spacefilling token order over na¨ıve flattening, as shown in Tab. [13.](#page-13-4) This is possibly because video encoders are initialized from image transformers, and block attention with reordering prevents video tokens from attending to other spatial locations from the same frame. We leave the study of an optimal chunking strategy for 3D inputs for future work. + +To measure the sensitivity of the learned video-text model to temporal cues, we perform an evaluation with shuffled input frames. Tab. [14](#page-14-1) shows a performance drop of **SViTT** models on retrieval (SSv2) and video QA (ActivityNet) tasks, indicating that the video-text models have learned to reason about the temporal dynamics of video clips. The difference ∆ between normal and shuffled in- + + + +| | | | SSv2-Label | | ActivityNet-QA | | | +|------------------|-----|------|------------|-----|----------------|------|-----| +| Model | Sp. | N | S | ∆ | N | S | ∆ | +| Singularity [27] | — | 66.6 | 66.3 | 0.3 | 41.8 | 41.8 | 0.0 | +| | D | 66.1 | 64.9 | 1.2 | 42.5 | 42.3 | 0.2 | +| SViTT | H | 69.3 | 65.8 | 3.5 | 43.2 | 42.3 | 0.9 | + +Table 14. Temporal Probing. Video-text transformers are evaluated using Normal and Shuffled frame order. + +puts is more prominent on hybrid sparse models, possibly because they attend more to the foreground which contains more temporal variations. Notably, this behavior does not hold for the Singularity model, whose performance is unaffected by frame order. This suggests that late temporal aggregation after spatial global pooing is insufficient to capture spatiotemporal relations across video frames. + +A person is standing, grasping their phone, then begins to tidy up the sofa, and begins to sneeze. + +![](_page_15_Figure_1.jpeg) + +A person is undressing and throwing their clothes on the sofa, then sitting on the edge of the bed. The person removes their socks. + +![](_page_15_Figure_3.jpeg) + +A person drinking a glass of water walks down the stairs at the bottom they sit down to take off their shoes and sneeze while untying the laces. + +![](_page_15_Figure_5.jpeg) + +Figure 9. Qualitative Results (continued). + +Figs. [8](#page-14-2) and [9](#page-15-1) visualizes the node sparsification patterns generated by visual encoder fv and multimodal encoder fm. While visual sparsification alone can significantly reduce the number of tokens during forward pass, we find that the cross-modal attention map aligns better with regions of interest in each clip, enabling greater node sparsity in videotext modeling. diff --git a/2305.14838/main_diagram/main_diagram.drawio b/2305.14838/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..d11cb90e1244d2b40bd852cddb600b1f41467390 --- /dev/null +++ b/2305.14838/main_diagram/main_diagram.drawio @@ -0,0 +1,22 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/2305.14838/paper_text/intro_method.md b/2305.14838/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..d3a33144790d4eaf0c12206d495031682de6385b --- /dev/null +++ b/2305.14838/paper_text/intro_method.md @@ -0,0 +1,82 @@ +# Introduction + +In recent years, an end-to-end (E2E) modeling approach has been widely applied to spoken language tasks, e.g., speech translation, speech summarization, speech understanding, etc. It enables us to train a single model by E2E optimization with spoken language task-oriented metrics. The conventional pipeline design generally contains two modules: speech model decodes the input speech into text and language model infers the recognized text to target language in translation task, topic sentence in summarization or intent in understanding task. These two modules are trained using their own respective criteria, which may not be optimal for each other. The cascaded process probably propagates errors that occurred in the current module to the following module. In addition, other information such as prosodic features contained in the speech signal, which are difficult to quantize using language symbols, can also be beneficial for spoken language tasks. + +Unified speech-language pretraining based on Transformer architecture has largely boosted E2E modeling for spoken language tasks [@fused; @ao2021speecht5; @tang2022unified; @slam; @mslam; @mu2slam; @maestro; @usm]. The pretraining is conducted jointly on unlabeled speech, unlabeled text, paired speech-to-text and paired text-to-text in multiple languages by using both self-supervised and supervised learning. The unified representations from both speech and text modalities are simultaneously learned via shared model parameters or auxiliary modality matching losses in the pretraining stage. However, on the other hand, pretrained speech-only and language-only models are also becoming increasingly powerful. To mitigate modality gap and achieve the comparable performance of the cascaded module system, unified speech-language pretraining must leverage the same or larger scale of data used in speech-only or language-only model pertaining. This makes the training process very challenging. + +In this paper, we propose **ComSL**: a **Com**posite **S**peech-**L**anguage Model for spoken language tasks. The main contributions of the paper can be summarized as follows: + +1. Our composite model fully leverages existing pretrained models, eliminating the need for pretraining with large amounts of data from scratch. It can be directly fine-tuned for downstream tasks and demonstrates its data efficiency. + +2. Our proposed cross-modality learning with speech-text mapping/matching on either representations or distributions is only based on the concatenation of paired speech and text. Unlike conventional approaches that use contrastive learning among the modalities, it does not require external or internal aligners to force-align speech and text at the token or word level. This simplifies the implementation and allows it to be incorporated into the fine-tuning stage. + +3. We have conducted a comprehensive ablation study on bridging the gap of speech and language representations, including tasks, losses, and strategies, as well as comparisons with previous works. + +4. Our model outperforms the SOTA Google USM model [@usm], Open AI Whisper model [@whisper], and cascaded non-E2E models by 0.8, 1.8 and 1.0 average BLUE score improvements on the CoVoST 2 evaluation set, respectively. + +# Method + +End-to-end speech translation directly translates speech from a source language into text in a target language, without generating intermediate ASR transcription. It can be formulated to find the most likely label sequence $\bold{y}=\{y_1,y_2,...,y_N\}$ (e.g., words or characters) of length $N$ in the target language given acoustic feature sequence $\bold{s}=\{s_1,s_2,...,s_T\}$ (e.g., Mel Filter Bank features) of length $T$ in the source language. An ST training corpus $\mathcal{D}^{\mathrm{ST}}=$ $\{(s, x, y)\}$ usually contains $\bold{x}=\{x_1,x_2,...,x_M\}$, the transcription of speech in source language, in addition to $\bold{y}$ and $\bold{s}$. It allows us to use MT and ASR as auxiliary tasks in a multi-task learning manner during the optimization of E2E ST model, as $$\begin{equation} +\label{eqn:problem} +% \mathcal{L} = -\sum_{i=1}^{|\bold{y}|} \log{\mathcal{P}(y_i | \bold{s}, \bold{y}_{ + +
Overall architecture of our model ComSL, consisting of speech Transformer Blocks, adapter, and language Transformer blocks. (a) model training with ASR, ST, MT and cross-modality learning (CML) tasks. (b) model inference only for ST tasks.
+ + +Followed by previous work [@fst; @li2020multilingual; @car], our model is a composite model, which is mainly composed of three components: speech Transformer blocks, an adapter and language Transformer blocks stacked together, shown in Figure [1](#fig:architecture){reference-type="ref" reference="fig:architecture"}. All of the components except the adapter are initialized by pretrained models. We also leverage multi-task learning for both speech and text inputs in the training stage. + +**Speech Transformer blocks** are initialized with the encoder parameters of Whisper model [@whisper][^3]. Whisper follows a standard sequence-to-sequence Transformer architecture [@Transformer] and has achieved strong performance on both speech-to-text recognition and translation tasks. It is trained on 680,000 hours of labeled speech recognition data as well as 125,000 hours of labeled X→en translation data. Here we only take its encoder as a powerful speech representation extractor. + +**Adapter** is placed between the speech Transformer blocks and the language Transformer blocks. It contains two layers, in which each layer is composed of a feed-forward module and a 1-D convolution layer with stride two. It achieves four times down-sampling for speech encoder outputs in total. And the non-linearity of the feed-forward module can speed up the adaptation of speech representations to be fed into language Transformer blocks. + +**Language Transformer blocks** are initialized with mBART model. mBART is composed of 12 layers of encoder and 12 layers of decoder with model dimension of 1024 on 16 heads. It is pretrained on monolingual data and fine-tuned on paired MT data and thus capable of many-to-many translation. All its parameters are used to enhance the translation capability of ComSL. + +In Equation [\[eqn:problem\]](#eqn:problem){reference-type="ref" reference="eqn:problem"}, we can feed speech-only input and text-only input into the model to learn the tasks of ST, ASR, and MT, respectively. Additionally, we introduce cross-modality learning (CML) based on paired speech-text input to minimize the gap between speech and text modalities. Different from the previous speech-text alignment approaches that rely on externally forced-alignment methods to determine word or other unit boundaries in speech sequences, our approach intrinsically learns cross-modality information during model optimization. As illustrated in Figure [2](#fig:cross-modality learning){reference-type="ref" reference="fig:cross-modality learning"}, the speech embedding, $e^s$, from adapter and text embeddings, $e^{x}$, from text tokenizer are **concatenated** as input to mBART encoder, $\bold{Enc^t}$. After traversing the encoder, the concatenated form is **split** into $\hat{z^s}$ and $\hat{z^x}$ again to pass through mBART decoder, formulating $$\begin{equation} + \hat{z^s}, \hat{z^x} = \bold{Enc^t}\left(e^s \oplus e^{x'}\right) +\end{equation}$$ where $e^{x'}$ is the masked text embedding and $\oplus$ denotes concatenation. The following tasks are used for cross-modality learning. + +![Illustration of cross-modality learning. The speech and text embeddings are concatenated into the mBART encoder for ERM loss and split into the decoder for STM and MTP tasks.](CML.png){#fig:cross-modality learning width="55%"} + +**Masked Token Prediction (MTP)** The text tokens in the concatenated input are randomly masked by a probability $p_{mask}$. The MTP task is to predict the masked tokens in the output of the decoder. In practice we still let the decoder generate the whole sentence teacher-forcingly but we only add loss on the masked tokens. $$\begin{equation} + \mathcal{L}_{MTP} = -\sum_{\mathcal{D}} \sum_{t=1 }^{M} \sum_{v=1}^{|V|} \mathbf{1}(x_t = v \& x'_t = \text{}) * \log{\mathcal{P}(x_t = v |x_{