pwc-archive/papers-with-abstracts huggingface dataset:
Lifting Imbalanced Regression with Self-Supervised Learning
Authors: Weiguo Pian, Hanyu Peng, Mingming Sun, Ping Li
Date: 2021-09-29 00:00:00
Tasks: imbalanced classification, regression, Self-Supervised Learning

A new influential task called imbalanced regression, most recently inspired by imbalanced classification, originating straightforwardly from both the imbalance and regression worlds, has received a great deal of attention. Yet we are still at a fairly preliminary stage in the exploration of this task, so more attempts are needed. In this paper, we work on a seamless marriage of imbalanced regression and self-supervised learning. But with this comes the first question of how to measure the similarity and dissimilarity under the regression sense, for which the definition is clear in the classification. To overcome the limitation, the formal definition of similarity in the regression task is given. On top of this, through experimenting on a simple neural network, we found that self-supervised learning could help alleviate the problem. However, the second problem is, it is not guaranteed that the noisy samples are similar to original samples when scaling to a deep network by adding random noise to the input, we specifically propose to limit the volume of noise on the output, and in doing so to find meaningful noise on the input by back propagation. Experimental results show that our approach achieves the state-of-the-art performance.
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations
Authors: Xiao Jing, Zhenwei Zhu, Hongliang Li, Xin Pei, Yoshua Bengio, Tong Che, Hongyong Song
Date: 2021-09-29 00:00:00
Tasks: Distributed Computing, Efficient Exploration

One of the greatest challenges of reinforcement learning is efficient exploration, especially when training signals are sparse or deceptive. The main difficulty of exploration lies in the size and complexity of the state space, which makes simple approaches such as exhaustive search infeasible. Our work is based on two important observations. On one hand, modern computing platforms are extremely scalable in terms of number of computing nodes and cores, which can complete asynchronous and well load-balanced computational tasks very fast. On the other hand,  Divide-and-Conquer is a commonly used technique in computer science to solve similar problems (such as SAT) of doing efficient search in extremely large state space. In this paper, we apply the idea of divide-and-conquer in the context of intelligent exploration. The resulting exploration scheme can be combined with various specific intrinsic rewards designed for the given task. In our exploration scheme, the learning algorithm can automatically divide the state space into regions, and each agent is assigned to explore one of these regions. All the agents run asynchronously and they can be deployed onto modern distributed computing platforms. Our experiments show that the proposed method is highly efficient and is able to achieve state-of-the-art results in many RL tasks such as MiniGrid and Vizdoom.
Variational Disentangled Attention for Regularized Visual Dialog
Authors: Jen-Tzung Chien, Hsiu-Wei Tien
Date: 2021-09-29 00:00:00
Tasks: Question Answering, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

One of the most important challenges in a visual dialog is to effectively extract the information from a given image and its historical conversation which are related to the current question. Many studies adopt the soft attention mechanism in different information sources due to its simplicity and ease of optimization. However, some of visual dialogs are observed in a single round. This implies that there is no substantial correlation between individual rounds of questions and answers. This paper presents a unified approach to disentangled attention to deal with context-free visual dialogs. The question is disentangled in latent representation. In particular, an informative regularization is imposed to strengthen the dependence between vision and language by pretraining on the visual question answering before transferring to visual dialog. Importantly, a novel variational attention mechanism is developed and implemented by a local reparameterization trick which carries out a discrete attention to identify the relevant conversations in a visual dialog. A set of experiments are evaluated to illustrate the merits of the proposed attention and regularization schemes for context-free visual dialogs.
Reference-Limited Compositional Learning: A Realistic Assessment for Human-level Compositional Generalization
Authors: Siteng Huang, Qiyao Wei, Donglin Wang
Date: 2021-09-29 00:00:00

To narrow the considerable gap between artificial and human intelligence, we propose a new task, namely reference-limited compositional learning (RLCL), which reproduces three core challenges to mimic human perception: compositional learning, few-shot, and few referential compositions. Building upon the setting, we propose two benchmarks that consist of multiple datasets with diverse compositional labels, providing a suitable and realistic platform for systematically assessing progress on the task. Moreover, we extend popular few-shot and compositional learning approaches to serve as baselines, and also introduce a simple method that achieves better performance in recognizing unseen compositions. Extensive experiments demonstrate that existing solutions struggle with the challenges imposed by the RLCL task, revealing substantial research space for pursuing human-level compositional generalization ability.
A Two-Stage Framework to Generate Video Chapter
Authors: Canyu Le, Zhiyuan Tang, Ke Li, Jiandong Yang
Date: 2021-09-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

We aim to address the problem of video chapter generation. 
Compared to traditional video activity analysis, this task is significantly different.
The videos in chapter generation are much longer and contain many complex temporal structures.
Moreover, the association between video frames and narrations plays a crucial role in expressing underlying information.
To facilitate the research along this direction, we introduce a large-scale dataset called ChapterGen, which consists of approximately $10k$ user-generated videos with annotated chapter descriptions. 
Our data collection procedure is fast, scalable, and does not require any additional manual annotation.
On top of this dataset, we propose a two-stage framework to perform chapter localization and chapter title generation. 
This framework captures two aspects of a video, including visual dynamics and narration text.
To parse the whole video efficiently, we build the framework based on a flexible clip sliding window.
Our experiments demonstrate that the proposed framework achieves superior results over existing methods on both accuracy and efficiency.
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains
Authors: Richard Arnoud Schoonhoven, Allard Hendriksen, Daniel Pelt, Joost Batenburg
Date: 2021-09-29 00:00:00
Tasks: Network Pruning

Neural network pruning techniques can substantially reduce the computational cost of applying convolutional neural networks (CNNs). Common pruning methods determine which convolutional filters to remove by ranking the filters individually, i.e., without taking into account their interdependence. In this paper, we advocate the viewpoint that pruning should consider the interdependence between series of consecutive operators. We propose the LongEst-chAiN (LEAN) method that prunes CNNs by using graph-based algorithms to select relevant chains of convolutions. A CNN is interpreted as a graph, with the operator norm of each operator as distance metric for the edges. LEAN pruning iteratively extracts the highest value path from the graph to keep. In our experiments, we test LEAN pruning on several image-to-image tasks, including the well-known CamVid dataset, and a real-world X-ray CT dataset. Results indicate that LEAN pruning can result in networks with similar accuracy but 3--20x fewer convolutional filters than networks pruned with methods that rank filters individually.
Contextual Text Detection
Authors: Chuhui Xue, Jiaxing Huang, Wenqing Zhang, Shijian Lu, Song Bai, Changhu Wang
Date: 2021-09-29 00:00:00
Tasks: Text Detection

Most existing scene text detectors focus on the detection of characters or words which capture partial textual messages only in most cases due to the missing of contextual information. For a better understanding of text in scenes, it is more desired to detect contextual text blocks which consist of one or multiple integral text units (e.g., characters, words, or phrases) in a specific order, delivering certain independent and complete textual messages. This paper presents Contextual Text Detection, a new setup that detects contextual text blocks for better understanding of texts in scenes. We formulate the new setup by a dual detection task that first detects integral text units and then groups them into a contextual text block. Specifically, we design a novel scene text grouping technique which treats each integral text unit as a token and groups multiple integral tokens belonging to the same contextual text block into an ordered token sequence. To facilitate the future research, we create two new datasets SCUT-CTW-Context and ReCTS-Context where each contextual text block is well annotated by an ordered sequence of integral text units. In addition, we introduce three evaluation metrics that measure contextual text detection in local accuracy, continuity, and global accuracy, respectively. Extensive experiments show that the proposed method detects contextual text blocks effectively. This development including codes, datasets and annotation tools will be published at http://xxxxxxx.
Visual Representation Learning over Latent Domains
Authors: Lucas Deecke, Timothy Hospedales, Hakan Bilen
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Fairness, Representation Learning

A fundamental shortcoming of deep neural networks is their specialization to a single task and domain. While multi-domain learning enables the learning of compact models that span multiple visual domains, these rely on the presence of domain labels, in turn requiring laborious curation of datasets. This paper proposes a less explored, but highly realistic new setting called latent domain learning: learning over data from different domains, without access to domain annotations. Experiments show that this setting is particularly challenging for standard models and existing multi-domain approaches, calling for new customized solutions: a sparse adaptation strategy is formulated which adaptively accounts for latent domains in data, and significantly enhances learning in such settings. Our method can be paired seamlessly with existing models, and boosts performance in conceptually related tasks, e.g. empirical fairness problems and long-tailed recognition.
S2C2 - An orthogonal method for Semi-Supervised Learning on ambiguous labels
Authors: Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Claudius Zelenka, Rainer Kiko, Jenny Stracke, Nina Volkmann, Reinhard Koch
Date: 2021-09-29 00:00:00

Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning. Most SSL methods assume a clear distinction between classes, but class boundaries are often ambiguous in real-world datasets due to intra- or interobserver variability. This ambiguity of annotations must be addressed as it will otherwise limit the performance of SSL and deep learning in general due to inconsistent label information. We propose SemiSupervised Classification & Clustering (S2C2) which can extend many deep SSL algorithms. S2C2 automatically estimates the ambiguity of an image and applies the respective SSL algorithm as a classification to certainly labeled data while partitioning the ambiguous data into clusters of visual similar images. We show that S2C2 results in a 7.6% better F1-score for classifications and 7.9% lower inner distance of clusters on average across multiple SSL algorithms and datasets. Moreover, the output of S2C2 can be used to decrease the ambiguity of labels with the help of human experts. Overall, a combination of Semi-Supervised Learning with our method S2C2 leads to better handling of ambiguous labels and thus realworld datasets.
Short-term memory in neural language models
Authors: Kristijan Armeni, Christopher Honey, Tal Linzen
Date: 2021-09-29 00:00:00
Tasks: Language Modelling, Retrieval

When a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. Thus, language models require mechanisms to maintain and access memory. Although we design the architectural features of these models, we do not know how their memory systems are functionally organized via learning: what kind of information about the prior context can they retrieve? We reasoned that access to arbitrary individual tokens from the past could be computationally powerful, akin to the  working memory which is important for flexible cognition in humans, and we therefore tested whether language models could ``retrieve'' the exact words that occurred previously in a text. In particular, we tested how the ability to retrieve prior words depended on (i) the number of words being retrieved, (ii) their semantic coherence, and (iii) the length and quality of the intervening text. We evaluated two particular architectures of neural language models: the attention-based transformer and the long short-term memory network (LSTM). In our paradigm, language models processed English text in which a list of nouns occurred twice. We operationalized retrieval as the reduction in surprisal from the first presentation of the list to its second presentation. We found that the transformer models retrieved both the identity and ordering of nouns from the first list. The transformer was successful even when the noun lists were semantically incoherent, and this effect was largely robust to the type or length of the intervening text. Further, the transformer’s retrieval was markedly enhanced when it was trained on a larger corpus and with greater model depth. Lastly, its ability to index prior tokens was dependent on learned attention patterns. In contrast, the LSTM models exhibited less precise retrieval (smaller reductions in surprisal). The LSTM’s retrieval was limited to list-initial tokens, and occurred only across short intervening texts. Moreover, the LSTM's retrieval was not sensitive to the order of nouns and this non-specific retrieval improved when the list was semantically coherent. In sum, the transformer, when trained to predict linguistic tokens, implements something akin to a working memory system, as it could flexibly retrieve individual token representations across arbitrary delays. Conversely, the LSTM maintained a coarser and more rapidly-decaying semantic gist of prior tokens, weighted heavily toward the earliest items.  Thus, although the transformer and LSTM architectures were both trained to predict language sequences, only the transformer learned to flexibly index prior tokens.
Accelerated Gradient-Free Method for Heavily Constrained Nonconvex Optimization
Authors: Wanli Shi, Hongchang Gao, Bin Gu
Date: 2021-09-29 00:00:00

Zeroth-order (ZO) method has been shown to be a powerful method for solving the optimization problem where explicit expression of the gradients is difficult or infeasible to obtain. Recently, due to the practical value of the constrained problems, a lot of ZO Frank-Wolfe or projected ZO methods have been proposed. However, in many applications, we may have a very large number of nonconvex white/black-box constraints, which makes the existing zeroth-order methods extremely inefficient (or even not working) since they need to inquire function value of all the constraints and project the solution to the complicated feasible set. In this paper, to solve the nonconvex problem with a large number of white/black-box constraints, we proposed a doubly stochastic zeroth-order gradient method (DSZOG). Specifically, we reformulate the problem by using the penalty method with distribution probability and sample a mini-batch of constraints to calculate the stochastic zeroth/first-order gradient of the penalty function to update the parameters and distribution, alternately. To further speed up our method, we propose an accelerated doubly stochastic zeroth-order gradient method (ADSZOG) by using the exponential moving average method and adaptive stepsize. Theoretically, we prove DSZOG and ADSZOG can converge to the $\epsilon$-stationary point of the constrained problem. We also compare the performances of our method with several ZO methods in two applications, and the experimental results demonstrate the superiority of our method in terms of training time and accuracy.
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL
Authors: Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang
Date: 2021-09-29 00:00:00
Tasks: Diversity, Multi-agent Reinforcement Learning, SMAC, SMAC+, Starcraft, Starcraft II

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attributes and goals. For example, in Starcraft II battle tasks, agents are initialized with the various move, defense, and attack abilities according to their unit types. Current researchers tend to treat different agents equally and expect them to form a joint policy automatically. However, ignoring the differences between agents in these scenarios may bring policy degradation. Accordingly, in this study, we quantify the agent's difference and study the relationship between the agent's role and the model performance via {\bf Role Diversity}, a metric that can describe MARL tasks. We define role diversity from three perspectives: policy-based, trajectory-based, and contribution-based to fully describe the agents' differences. Through theoretical analysis, we find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity. The decomposed factors can significantly impact policy optimization on parameter sharing, communication mechanism, and credit assignment strategy. Role diversity can therefore serve as a flag for selecting a suitable training strategy and helping to avoid possible bottlenecks on current tasks. The main experimental platforms are based on {\bf Multiagent Particle Environment (MPE) }and {\bf The StarCraft Multi-Agent Challenge (SMAC)}, with extensions to ensure the requirement of this study are met. Our experimental results clearly show that role diversity can serve as a robust description for the characteristics of a multi-agent cooperation task and help explain the question of why the performance of different MARL training strategies is unstable according to this description. In addition, role diversity can help to find a better training strategy and increase performance in cooperative MARL.
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?
Authors: Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
Date: 2021-09-29 00:00:00
Tasks: Image Generation, Transfer Learning

Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks. The most significant advantage of using synthetic images is that the ground-truth labels are automatically available, enabling unlimited data size expansion without human cost. However, synthetic data may have a huge domain gap, in which case increasing the data size does not improve the performance. How can we know that? In this study, we derive a simple scaling law that predicts the performance from the amount of pre-training data. By estimating the parameters of the law, we can judge whether we should increase the data or change the setting of image synthesis. Further, we analyze the theory of transfer learning by considering learning dynamics and confirm that the derived generalization bound is compatible with our empirical findings. We empirically validated our scaling law on various experimental settings of benchmark tasks, model sizes, and complexities of synthetic images.
Value Refinement Network (VRN)
Authors: Jan Wöhlke, Felix Schmitt, Herke van Hoof
Date: 2021-09-29 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

Sparse rewards and long decision horizons make agent navigation tasks difficult to solve via reinforcement learning (RL) such as (deep) Q-learning. Previous work has shown that some of these tasks are efficiently solvable by value-based planning in a state space abstraction, which defines sub-goals for a policy in the original state space. However, value-based planning scales poorly with the number of state space dimensions. Consequently, the planning might not be able to consider all the state information, like other agents' behaviors. Combining the benefits of planning and learning values, we propose the Value Refinement Network (VRN), an architecture that locally refines a plan in a (simpler) state space abstraction, represented by a pre-computed value function, with respect to the full agent state. Training the VRN via RL, it can learn how to correct this initial plan effectively to solve tasks that otherwise would require a prohibitively large abstraction. Evaluating on several simulated agent navigation tasks, we demonstrate the benefits of our VRN: We show that it can successfully refine shortest path plans to match the performance of value iteration in a more complex state space. Furthermore, in vehicle parking tasks where considering all relevant state space dimensions in planning is infeasible, the VRN still enables high task completion rates.
$\alpha$-Weighted Federated Adversarial Training
Authors: Jianing Zhu, Jiangchao Yao, Tongliang Liu, Kunyang Jia, Jingren Zhou, Bo Han, Hongxia Yang
Date: 2021-09-29 00:00:00
Tasks: Adversarial Attack, Federated Learning

Federated Adversarial Training (FAT) helps us address the data privacy and governance issues, meanwhile maintains the model robustness to the adversarial attack. However, the inner-maximization optimization of Adversarial Training can exacerbate the data heterogeneity among local clients, which triggers the pain points of Federated Learning. This makes that the straightforward combination of two paradigms shows the performance deterioration as observed in previous works. In this paper, we introduce an $\alpha$-Weighted Federated Adversarial Training ($\alpha$-WFAT) method to overcome this problem, which relaxes the inner-maximization of Adversarial Training into a lower bound friendly to Federated Learning. We present the theoretical analysis about this $\alpha$-weighted mechanism and its effect on the convergence of FAT. Empirically, the extensive experiments are conducted to comprehensively understand the characteristics of $\alpha$-WFAT, and the results on three benchmark datasets demonstrate $\alpha$-WFAT significantly outperforms FAT under different adversarial learning methods and federated optimization methods.