GenAI4ELab/papercli-papers huggingface dataset:
Hierarchical Prompting Improves Visual Recognition On Accuracy, Data Efficiency and Explainability
Authors: Wenhao Wang; Yifan Sun; Wei Li; Yi Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: When humans try to distinguish some inherently similar visual concepts, e.g., Rosa Peace and China Rose, they may use the underlying hierarchical taxonomy to prompt the recognition. For example, given a prompt that the image belongs to the rose family, a person can narrow down the category range and thus focuses on the comparison between different roses. In this paper, we explore the hierarchical prompting for deep visual recognition (image classification, in particular) based on the prompting mechanism of the transformer. We show that the transformer can take the similar benefit by injecting the coarse-class prompts into the intermediate blocks. The resulting Transformer with Hierarchical Prompting (TransHP) is very simple and consists of three steps: 1) TransHP learns a set of prompt tokens to represent the coarse classes, 2) learns to predict the coarse class of the input image using an intermediate block, and 3) absorbs the prompt token of the predicted coarse class into the feature tokens. Consequently, the injected coarse-class prompt conditions (influences) the subsequent feature extraction and encourages better focus on the relatively subtle differences among the descendant classes. Through extensive experiments on popular image classification datasets, we show that this simple hierarchical prompting improves visual recognition on classification accuracy (e.g., improving ViT-B/16 by $+2.83\%$ ImageNet classification accuracy), training data efficiency (e.g., $+12.69\%$ improvement over the baseline under $10\%$ ImageNet training data), and model explainability.
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks
Authors: Blake Bordelon; Cengiz Pehlevan
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: It is unclear how changing the learning rule of a deep neural network alters its learning dynamics and representations. To gain insight into the relationship between learned features, function approximation, and the learning rule, we analyze infinite-width deep networks trained with gradient descent (GD) and biologically-plausible alternatives including feedback alignment (FA), direct feedback alignment (DFA), and error modulated Hebbian learning (Hebb), as well as gated linear networks (GLN). We show that, for each of these learning rules, the evolution of the output function at infinite width is governed by a time varying effective neural tangent kernel (eNTK). In the lazy training limit, this eNTK is static and does not evolve, while in the rich mean-field regime this kernel's evolution can be determined self-consistently with dynamical mean field theory (DMFT). This DMFT enables comparisons of the feature and prediction dynamics induced by each of these learning rules. In the lazy limit, we find that DFA and Hebb can only learn using the last layer features, while full FA can utilize earlier layers with a scale determined by the initial correlation between feedforward and feedback weight matrices. In the rich regime, DFA and FA utilize a temporally evolving and depth-dependent NTK. Counterintuitively, we find that FA networks trained in the rich regime exhibit more feature learning if initialized with smaller correlation between the forward and backward pass weights. GLNs admit a very simple formula for their lazy limit kernel and preserve conditional Gaussianity of their preactivations under gating functions. Error modulated Hebb rules show very small task-relevant alignment of their kernels and perform most task relevant learning in the last layer.
Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation
Authors: Gyan Tatiya; Jonathan Francis; Luca Bondi; Ingrid Navarro; Eric Nyberg; Jivko Sinapov; Jean Oh
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, generalisation includes both generalising to unseen indoor visual scenes as well as generalising to unheard sounding objects. Previous SAVi task definitions do not include evaluation conditions on truly novel sounding objects, resorting instead to evaluating agents on unheard sound clips of known objects; meanwhile, previous SAVi methods do not include explicit mechanisms for incorporating domain knowledge about object and region semantics. These weaknesses limit the development and assessment of models' abilities to generalise their learned experience. In this work, we introduce the use of knowledge-driven scene priors in the semantic audio-visual embodied navigation task: we combine semantic information from our novel knowledge graph that encodes object-region relations, spatial knowledge from dual Graph Encoder Networks, and background knowledge from a series of pre-training tasks---all within a reinforcement learning framework for audio-visual navigation. We define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects. We show improvements over strong baselines in generalisation to unseen regions and novel sounding objects, within the Habitat-Matterport3D simulation environment, under the SoundSpaces task. We release code, knowledge graphs, and dataset generation details in the supplementary material.
Learning Vortex Dynamics for Fluid Inference and Prediction
Authors: Yitong Deng; Hong-Xing Yu; Jiajun Wu; Bo Zhu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning the observable, Eulerian flow phenomena. Our differentiable vortex particles are coupled with a learnable, vortex-to-velocity dynamics mapping to effectively capture the complex flow features in a physically-constrained, low-dimensional space. This representation facilitates the learning of a fluid simulator tailored to the input video that can deliver robust, long-term future predictions. The value of our method is twofold: first, our learned simulator enables the inference of hidden physics quantities (e.g., velocity field) purely from visual observation; secondly, it also supports future prediction, constructing the input video's sequel along with its future dynamics evolution. We compare our method with a range of existing methods on both synthetic and real-world videos, demonstrating improved reconstruction quality, visual plausibility, and physical integrity.
Learning Intuitive Policies Using Action Features
Authors: Mingwei Ma; Jizhou Liu; Samuel Sokota; Max Kleiman-Weiner; Jakob Nicolaus Foerster
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: An unaddressed challenge in multi-agent coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations. Humans take advantage of these relationships in highly intuitive ways. For instance, in the absence of a shared language, we might point to the object we desire or hold up our fingers to indicate how many objects we want. To address this challenge, we investigate the effect of network architecture on the propensity of learning algorithms to exploit these semantic relationships. Across a procedurally generated coordination task, we find that attention-based architectures that jointly process a featurized representation of observations and actions have a better inductive bias for zero-shot coordination. Through fine-grained evaluation and scenario analysis, we show that the resulting policies are human-interpretable. Moreover, such agents coordinate with people without training on any human data.
Few-Shot Incremental Learning Using HyperTransformers
Authors: Max Vladymyrov; Andrey Zhmoginov; Mark Sandler
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Incremental few-shot learning methods make it possible to learn without forgetting from multiple few-shot tasks arriving sequentially. In this work we approach this problem using the recently published HyperTransformer (HT): a hypernetwork that generates task-specific CNN weights directly from the support set. We propose to re-use these generated weights as an input to the HT for the next task of the continual-learning sequence. Thus, the HT uses the weights themselves as the representation of the previously learned tasks. This approach is different from most continual learning algorithms that typically rely on using replay buffers, weight regularization or task-dependent architectural changes. Instead, we show that the HT works akin to a recurrent model, relying on the weights from the previous task and a support set from a new task. We demonstrate that a single HT equipped with a prototypical loss is capable of learning and retaining knowledge about past tasks for two continual learning scenarios: incremental-task learning and incremental-class learning.
Amortised Invariance Learning for Contrastive Self-Supervision
Authors: Ruchika Chavhan; Jan Stuehmer; Calum Heggan; Mehrdad Yaghoobi; Timothy Hospedales
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances established during pre-training can be interpreted as strong inductive biases. However these may or may not be helpful, depending on if they match the invariance requirements of  downstream tasks or not. This has led to several attempts to learn task-specific invariances during pre-training, however, these methods are highly compute intensive and tedious to train. We introduce the notion of amortized invariance learning for contrastive self supervision. In the pre-training stage, we parameterize the feature extractor by differentiable invariance hyper-parameters that control the invariances encoded by the representation. Then, for any downstream task, both linear readout and task-specific invariance requirements can be efficiently and effectively learned by gradient-descent. We evaluate the notion of amortized invariances for contrastive learning over two different modalities: vision and audio, on two widely-used contrastive learning methods in vision: SimCLR and MoCo-v2 with popular architectures like ResNets and Vision Transformers, and SimCLR with ResNet-18 for audio. We show that our amortized features provide a reliable way to learn diverse downstream tasks with different invariance requirements, while using a single feature and avoiding task-specific pre-training. This provides an exciting perspective that opens up new horizons in the field of general purpose representation learning.
Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions
Authors: Mingu Lee; Saurabh Pitre; Tianyu Jiang; Pierre-David Letourneau; Matthew J Morse; Kanghwan Jang; Joseph Soriaga; Parham Noorzad; Hsin-Pai Cheng; Christopher Lott
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Since the introduction of Transformers, researchers have tackled the notoriously expensive quadratic complexity problem.  While significant computational efficiency improvements have been achieved, they come at the cost of reduced accuracy trade-offs. In this paper, we propose Composite Slice Transformer (CST), a Transformer-based network equipped with a composition of multi-scale multi-range attentions, boosting both efficiency and modeling capability.
After stacking fixed-length slices of the input sequence, each layer in CST performs a pair of fine-and-coarse-grained attentions with short-long ranges in a sequential manner, coupled with volatile instant positional embedding, enabling efficient token interactions {\em and} improving expressiveness of the model.
In addition to significantly reduced $O(NL+N^2/L^2)$ complexity for sequence length $N$ and slice length $L$, CST achieves superior performance on a variety of tasks.  We show that CST surpasses recently published efficient Transformers on the Long Range Arena benchmark, demonstrating the bidirectional long-range dependency modeling capability of our model. It outperforms the standard Transformer by a margin of $6.9$\% in average accuracy across the five classification tasks of the benchmark, while being of complexity comparable to other efficient transformers. Furthermore, on the word-level autoregressive language modeling task with the WikiText-103 dataset, CST performs competitively against the Transformer model with only $2$\% gap in the test perplexity while outperforming other efficient Transformers.
Example-based Planning via Dual Gradient Fields
Authors: Mingdong Wu; fangwei zhong; Yizhou Wang; Hao Dong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Path planning is one of the key abilities of an intelligent agent. However, both the learning-based and sample-based planners remain to require explicitly defining the task by manually designing the reward function or optimisation objectives, which limits the scope of implementation. Formulating the path planning problem from a new perspective, Example-based planning is to find the most efficient path to increase the likelihood of the target distribution by giving a set of target examples. In this work, we introduce Dual Gradient Fields (DualGFs), an offline-learning example-based planning framework built upon score matching. There are two gradient fields in DualGFs: a target gradient field that guides task completion and a support gradient field that ensures moving with environmental constraints. In the learning process, instead of interacting with the environment, the agents are trained with two offline examples, i.e., the target gradients and support gradients are trained by target examples and support examples, respectively. The support examples are randomly sampled from free space, e.g., states without collisions. DualGF is a weighted mixture of the two fields, combining the merits of the two fields together. To update the mixing ratio adaptively, we further propose a fields-balancing mechanism based on Lagrangian-Relaxation. Experimental results across four tasks (navigation, tracking, particle rearrangement, and room rearrangement) demonstrate the scalability and effectiveness of our method.
Prototypical Calibration for Few-shot Learning of Language Models
Authors: Zhixiong Han; Yaru Hao; Li Dong; Yutao Sun; Furu Wei
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical calibration to adaptively learn a more robust decision boundary for zero- and few-shot classification, instead of greedy decoding. Concretely, our method first adopts Gaussian mixture distribution to estimate the prototypical clusters for all categories. Then we assign each cluster to the corresponding label by solving a weighted bipartite matching problem. Given an example, its prediction is calibrated by the likelihood of prototypical clusters. Experimental results show that prototypical calibration yields a substantial improvement on a diverse set of tasks. Extensive analysis across different scales also indicates that our method calibrates the decision boundary as expected, greatly improving the robustness of GPT to templates, permutations, and class imbalance.
IS SYNTHETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION?
Authors: Ruifei He; Shuyang Sun; Xin Yu; Chuhui Xue; Wenqing Zhang; Philip Torr; Song Bai; XIAOJUAN QI
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in the data-scare settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.
Decompose to Generalize: Species-Generalized Animal Pose Estimation
Authors: Guangrui Li; Yifan Sun; Zongxin Yang; Yi Yang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper challenges the cross-species generalization problem for animal pose estimation, aiming to learn a pose estimator that can be well generalized to novel species. We find the relation between different joints is important with two-fold impact: 1) on the one hand, some relation is consistent across all the species and may help two joints mutually confirm each other, e.g., the eyes help confirm the nose and vice versa because they are close in all species. 2) on the other hand, some relation is inconsistent for different species due to the species variation and may bring severe distraction rather than benefit. With these two insights, we propose a Decompose-to-Generalize (D-Gen) pose estimation method to break the inconsistent relations while preserving the consistent ones. Specifically, D-Gen first decomposes the body joints into several joint concepts so that each concept contains multiple closely-related joints. Given these joint concepts, D-Gen 1) promotes the interaction between intra-concept joints to enhance their reliable mutual confirmation, and 2) suppresses the interaction between inter-concept joints to prohibit their mutual distraction.  Importantly, we explore various decomposition approaches, i.e., heuristic, geometric and attention-based approaches. Experimental results show that all these decomposition manners yield reasonable joint concepts and substantially improve cross-species generalization (and the attention-based approach is the best).
Axiomatic Explainer Locality With Optimal Transport
Authors: Joshua Bone; Aria Masoomi; Jennifer Dy
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Explainability methods have been notoriously difficult to evaluate and compare. Because of this, practitioners are often left guessing as to which explainer they should use for their task. Locality is one critical property of explainers which grants insight into the diversity of produced explanations. In this paper, we define a set of axioms which align with natural intuition regarding globalness, the inverse of locality. We then introduce a novel measure of globalness, Wasserstein Globalness, which uses optimal transport to quantify how local or global a given explainer is. Finally, we provide theoretical results describing the sample complexity of Wasserstein Globalness, and experimentally demonstrate how globalness can be used to effectively compare explainers. These results illustrate connections between both explainer fidelity and explainer robustness.
Understanding Adversarial Transferability in Federated Learning
Authors: Yijiang Li; ying gao; Dawn Song; Haohan Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: With the promises Federated Learning (FL) delivers, various topics regarding its robustness and security issues have been widely studied in recent years: such as the possibility to conduct adversarial attacks (or transferable adversarial attacks) in a while-box setting with full knowledge of the model (or the entire data), or the possibility to conduct poisoning/backdoor attacks during the training process as a malicious client. In this paper, we investigate the robustness and security issues from a different, simpler, but practical setting: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients, and only revealing their adversary position after the training to conduct transferable adversarial attacks with their data, which is usually a subset of the data that FL system is trained with. Our aim is to offer a full understanding of the challenges the FL system faces in this setting across a spectrum of configurations. We notice that such an attack is possible, but the federated model is more robust compared with its centralized counterpart when the accuracy on clean images is comparable. Through our study, we hypothesized the robustness is from two factors: the decentralized training on distributed data and the averaging operation. Our work has implications for understanding the robustness of federated learning systems and poses a practical question for federated learning applications.
Clustering for directed graphs using parametrized random walk diffusion kernels
Authors: Harry Sevi; Matthieu Jonckheere; Argyris Kalogeratos
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Clustering based on the random walk operator has been proven effective for undirected graphs, but its generalization to directed graphs (digraphs) is much more challenging. Although the random walk operator is well-defined for digraphs, in most cases such graphs are not strongly connected, and hence the associated random walks are not irreducible, which is a crucial property for clustering that exists naturally in the undirected setting. To remedy this, the usual workaround is to either naively symmetrize the adjacency matrix or to replace the natural random walk operator by the teleporting random walk operator, but this can lead to the loss of valuable information carried by edge directionality. In this paper, we introduce a new clustering framework,  the Parametrized Random Walk Diffusion Kernel Clustering (P-RWDKC), which is suitable for handling both directed and undirected graphs. Our framework is based on the diffusion geometry and the generalized spectral clustering framework. Accordingly, we propose an algorithm that automatically reveals the cluster structure at a given scale, by considering the random walk dynamics associated with a parametrized kernel operator, and by estimating its critical diffusion time. Experiments on $K$-NN graphs constructed from real-world datasets and real-world graphs show that our clustering approach performs well in all tested cases, and outperforms existing approaches in most of them.
Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform
Authors: Yubei Chen; Zeyu Yun; Yi Ma; Bruno Olshausen; Yann LeCun
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We describe a minimalistic and interpretable method for unsupervised representation learning that does not require data augmentation, hyperparameter tuning, or other engineering designs, but nonetheless achieves performance close to the state-of-the-art (SOTA) SSL methods. Our approach leverages the sparse manifold transform, which unifies sparse coding, manifold learning, and slow feature analysis. With a one-layer deterministic (one training epoch) sparse manifold transform, it is possible to achieve $99.3\%$ KNN top-1 accuracy on MNIST, $81.1\%$ KNN top-1 accuracy on CIFAR-10, and $53.2\%$ on CIFAR-100. With simple gray-scale augmentation, the model achieves $83.2\%$ KNN top-1 accuracy on CIFAR-10 and $57\%$ on CIFAR-100. These results significantly close the gap between simplistic ``white-box'' methods and SOTA methods. We also provide visualization to illustrate how an unsupervised representation transform is formed. The proposed method is closely connected to latent-embedding self-supervised methods and can be treated as the simplest form of VICReg. Though a small performance gap remains between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised representation learning, which has potential to significantly improve learning efficiency.