pwc-archive/papers-with-abstracts huggingface dataset:
Disentanglement, Visualization and Analysis of Complex Features in DNNs
Authors: Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang
Date: 2021-01-01 00:00:00
Tasks: Disentanglement, Knowledge Distillation

This paper aims to define, visualize, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method disentangles and visualizes feature components of different complexity orders from the feature. The disentanglement of feature components enables us to evaluate the reliability, the effectiveness, and the significance of over-fitting of these feature components. Furthermore, such analysis helps to improve the performance of DNNs. As a generic method, the feature complexity also provides new insights into existing deep-learning techniques, such as network compression and knowledge distillation. We will release the code when the paper is accepted.
Mirror Sample Based Distribution Alignment for Unsupervised Domain Adaption
Authors: Yin Zhao, Minquan Wang, Longjun Cai
Date: 2021-01-01 00:00:00
Tasks: Domain Adaptation, Learning Theory

Unsupervised Domain Adaption has great value in both machine learning theory and applications.
The core issue is how to minimize the domain shift. Motivated by the more and more sophisticated distribution alignment methods in sample level, we introduce a novel concept named (virtual) mirror, which represents the counterpart sample in the other domains.The newly-introduced mirror loss using the virtual mirrors establishes the connection cross domains and pushes the virtual mirror pairs together in the aligned representation space. Our proposed method does not align the samples cross domains coarsely or arbitrarily, thus does not distort the internal distribution of the underline distribution and brings better asymptotic performances.
Experiments on several benchmarks validate the superior performance of our methods.
Fast Estimation for Privacy and Utility in Differentially Private Machine Learning
Authors: Yuzhe Li, Yong liu, Weipinng Wang, Bo Li, Nan Liu
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning

Recently, differential privacy has been widely studied in machine learning due to its formal privacy guarantees for data analysis. As one of the most important parameters of differential privacy, $\epsilon$ controls the crucial tradeoff between the strength of the privacy guarantee and the utility of model. Therefore, the choice of $\epsilon$ has a great influence on the performance of differentially private learning models. But so far, there is still no rigorous method for choosing $\epsilon$. In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value. We show that our approximate approach has a fairly small error and can be used to estimate the optimal $\epsilon$ according to the expected utility of users. Experimental results demonstrate high estimation accuracy and broad applicability of our approximate approach.
Rethinking Pseudo-labeled Sample Mining for Semi-Supervised Object Detection
Authors: Duo Li, Sanli Tang, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Wenming Tan, Fei Wu, Xiaokang Yang
Date: 2021-01-01 00:00:00
Tasks: object-detection, Object Detection, Semi-Supervised Object Detection

Consistency-based method has been proved effective for semi-supervised learning (SSL). However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL. An intuitive idea is to select pseudo-labeled training samples by threshold. We find it essential the selection of these thresholds to the final result of SSL. Following this discovery, we propose SEAT (Score Ensemble with Adaptive Threshold), a simple and efficient semi-supervised learning object detection method, in which the high confidence pseudo-labels are selected for self-training. Apart from confidence score as the indicator of the sample's quality, we also introduce the scores of temporal consistency and augmentation consistency. The scores provide a more comprehensive description to the quality of each sample. To cope with the data distribution difference among categories, the adaptive threshold strategy is used to automatically determine the sample mining threshold for each category. We conduct experiments on PASCAL-VOC and MSCOCO, extensive results show that our method is competitive and can be easily combined with consistency-based methods.
Quantile Regularization : Towards Implicit Calibration of Regression Models
Authors: Saiteja Utpala, Piyush Rai
Date: 2021-01-01 00:00:00
Tasks: regression

Recent works have shown that most deep learning models are often poorly  calibrated, i.e., they may produce overconfident
predictions that are wrong, implying that their uncertainty estimates are unreliable. While a  number of approaches have been proposed recently to calibrate classification models, relatively little work exists on calibrating regression models. Isotonic Regression has recently been advocated for regression calibration. We provide a detailed formal analysis of the \emph{side-effects} of Isotonic Regression when used for regression  calibration. To address this, we  recast quantile calibration as entropy estimation, and leverage this idea to construct a novel quantile regularizer, which can be used in any optimization based probabilisitc regression models. Unlike most of the existing approaches for calibrating regression models, which are based on \emph{post-hoc} processing of the model's output, and require an additional dataset, our method is trainable in an end-to-end fashion, without requiring an additional dataset. We provide empirical results demonstrating that our approach improves  calibration for regression models trained on diverse architectures that  provide uncertainty estimates, such as Dropout VI, Deep Ensembles
Multi-Agent Trust Region Learning
Authors: Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang
Date: 2021-01-01 00:00:00
Tasks: Atari Games, MuJoCo, Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Trust-region methods are widely used in single-agent reinforcement learning.  One advantage is that they guarantee a lower bound of monotonic payoff improvement for policy optimization at each iteration. Nonetheless, when applied in multi-agent settings, such guarantee is lost because an agent's payoff is also determined by other agents' adaptive behaviors.  In fact, measuring agents' payoff improvements in multi-agent reinforcement learning (MARL) scenarios is still challenging. Although game-theoretical solution concepts such as Nash equilibrium can be applied, the algorithm (e.g., Nash-Q learning) suffers from poor scalability beyond two-player discrete games. To mitigate the above measurability and tractability issues, in this paper, we propose Multi-Agent Trust Region Learning (MATRL) method. MATRL augments the single-agent trust-region optimization process with the multi-agent solution concept of stable fixed point that is computed at the policy-space meta-game level. When multiple agents learn simultaneously, stable fixed points at the meta-game level can effectively measure agents' payoff improvements, and, importantly, a meta-game representation enjoys better scalability for multi-player games.  We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points. We evaluate the MATRL method on both discrete and continuous multi-player general-sum games; results suggest that MATRL significantly outperforms strong MARL baselines on grid worlds, multi-agent MuJoCo, and Atari games.
Certified Distributional Robustness via Smoothed Classifiers
Authors: Jungang Yang, Liyao Xiang, Ruidong Chen, Yukun Wang, Wei Wang, Xinbing Wang
Date: 2021-01-01 00:00:00

The robustness of deep neural networks against adversarial example attacks has received much attention recently. We focus on certified robustness of smoothed classifiers in this work, and propose to use the worst-case population loss over noisy inputs as a robustness metric. Under this metric, we provide a tractable upper bound serving as a robustness certificate by exploiting the duality. To improve the robustness, we further propose a noisy adversarial learning procedure to minimize the upper bound following the robust optimization framework. The smoothness of the loss function ensures the problem easy to optimize even for non-smooth neural networks. We show how our robustness certificate compares with others and the improvement over previous works. Experiments on a variety of datasets and models verify that in terms of empirical accuracies, our approach exceeds the state-of-the-art certified/heuristic methods in defending adversarial examples.
Global Self-Attention Networks
Authors: Zhuoran Shen, Irwan Bello, Raviteja Vemulapalli, Xuhui Jia, Ching-Hui Chen
Date: 2021-01-01 00:00:00
Tasks: Video Understanding

Recently, a series of works in computer vision have shown promising results on various image and video understanding tasks using self-attention. However, due to the quadratic computational and memory complexities of self-attention, these works either apply attention only to low-resolution feature maps in later stages of a deep network or restrict the receptive field of attention in each layer to a small local region. To overcome these limitations, this work introduces a new global self-attention module, referred to as the GSA module, which is efficient enough to serve as the backbone component of a deep network. This module consists of two parallel layers: a content attention layer that attends to pixels based only on their content and a positional attention layer that attends to pixels based on their spatial locations. The output of this module is the sum of the outputs of the two layers. Based on the proposed GSA module, we introduce new standalone global attention-based deep networks that use GSA modules instead of convolutions to model pixel interactions. Due to the global extent of the proposed GSA module, a GSA network has the ability to model long-range pixel interactions throughout the network. Our experimental results show that GSA networks outperform the corresponding convolution-based networks significantly on the CIFAR-100 and ImageNet datasets while using less number of parameters and computations. The proposed GSA networks also outperform various existing attention-based networks on the ImageNet dataset.
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION
Authors: Ali Borji
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Data Augmentation, Object Recognition

Deep object recognition models have been very successful over benchmark
datasets such as ImageNet. How accurate and robust are they to distribution
shifts arising from natural and synthetic variations in datasets? Prior research on
this problem has primarily focused on ImageNet variations (e.g., ImageNetV2,
ImageNet-A). To avoid potential inherited biases in these studies, we take a
different approach. Specifically, we reanalyze the ObjectNet dataset recently
proposed by Barbu et al. containing objects in daily life situations. They showed
a dramatic performance drop of the state of the art object recognition models on
this dataset. Due to the importance and implications of their results regarding
the generalization ability of deep models, we take a second look at their analysis.
We find that applying deep models to the isolated objects, rather than the entire
scene as is done in the original paper, results in around 20-30% performance
improvement. Relative to the numbers reported in Barbu et al., around 10-15%
of the performance loss is recovered, without any test time data augmentation.
Despite this gain, however, we conclude that deep models still suffer drastically
on the ObjectNet dataset. We also investigate the robustness of models against
synthetic image perturbations such as geometric transformations (e.g., scale,
rotation, translation), natural image distortions (e.g., impulse noise, blur) as well
as adversarial attacks (e.g., FGSM and PGD-5). Our results indicate that limiting
the object area as much as possible (i.e., from the entire image to the bounding
box to the segmentation mask) leads to consistent improvement in accuracy and
robustness. Finally, through a qualitative analysis of ObjectNet data, we find that
i) a large number of images in this dataset are hard to recognize even for humans,
and ii) easy (hard) samples for models match with easy (hard) samples for humans.
Overall, our analysis shows that ObjecNet is still a challenging test platform that
can be used to measure the generalization ability of models. The code and data
are available in [masked due to blind review].
GSdyn: Learning training dynamics via online Gaussian optimization with gradient states
Authors: Haoran Liao, Junchi Yan, Zimin Feng
Date: 2021-01-01 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization, whose efficiency for automatic hyperparameter tuning has been verified over the decade, still faces a standing dilemma between massive consumption of time and suboptimal search results. Although much effort has been devoted to accelerate and improve the optimizer, the dominantly time-consuming step of evaluation receives relatively less attention. In this paper, we propose a novel online Bayesian algorithm, which optimizes hyperparameters and learns the training dynamics to make it free from the repeated complete evaluations. To solve the non-stationary problem i.e. the same hyperparameters will lead to varying results at different training steps, we combine the training loss and the dominant eigenvalue to track training dynamics. Compared to traditional algorithms, it saves time and utilizes the important intermediate information which are not well leveraged by classical Bayesian methods that only focus on the final results. The performance on CIFAR-10 and CIFAR-100 verifies the efficacy of our approach.
Semantically-Adaptive Upsampling for Layout-to-Image Translation
Authors: Hao Tang, Nicu Sebe
Date: 2021-01-01 00:00:00
Tasks: Translation

We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task. SA-UpSample has three advantages: 1) Global view. Unlike traditional upsampling methods (e.g., Nearest-neighbor) that only exploit local neighborhoods, SA-UpSample can aggregate semantic information in a global view. 
2) Semantically adaptive.  Instead of using a fixed kernel for all locations (e.g., Deconvolution), SA-UpSample enables semantic class-specific upsampling via generating adaptive kernels for different locations. 3) Efficient. Unlike Spatial Attention which uses a fully-connected strategy to connect all the pixels, SA-UpSample only considers the most relevant pixels, introducing little computational overhead. We observe that SA-UpSample achieves consistent and substantial gains on six popular datasets. The source code will be made publicly available.
Transferability of Compositionality
Authors: Yuanpeng Li, Liang Zhao, Joel Hestness, Ka Yee Lun, Kenneth Church, Mohamed Elhoseiny
Date: 2021-01-01 00:00:00
Tasks: Out-of-Distribution Generalization

Compositional generalization is the algebraic capacity to understand and produce large amount of novel combinations from known components. It is a key element of human intelligence for out-of-distribution generalization. To equip neural networks with such ability, many algorithms have been proposed to extract compositional representations from the training distribution. However, it has not been discussed whether the trained model can still extract such representations in the test distribution. In this paper, we argue that the extraction ability does not transfer naturally, because the extraction network suffers from the divergence of distributions. To address this problem, we propose to use an auxiliary reconstruction network with regularized hidden representations as input, and optimize the representations during inference. The proposed approach significantly improves accuracy, showing more than a 20% absolute increase in various experiments compared with baselines. To our best knowledge, this is the first work to focus on the transferability of compositionality, and it is orthogonal to existing efforts of learning compositional representations in training distribution. We hope this work will help to advance compositional generalization and artificial intelligence research.
Batch Reinforcement Learning Through Continuation Method
Authors: Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: global-optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.  Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efficiently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation.  By constraining the difference between the learned policy and the behavior policy that generates the fixed trajectories, and continuously relaxing the constraint, our method 1) helps the agent escape local optima; 2) reduces the error in policy evaluation in the optimization procedure.   We present results on a variety of control tasks, game environments, and a recommendation task to empirically demonstrate the efficacy of our proposed method.
Protecting DNNs from Theft using an Ensemble of Diverse Models
Authors: Sanjay Kariyappa, Atul Prakash, Moinuddin K Qureshi
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: image-classification, Image Classification

Several recent works have demonstrated highly effective model stealing (MS) attacks on Deep Neural Networks (DNNs) in black-box settings, even when the training data is unavailable. These attacks typically use some form of Out of Distribution (OOD) data to query the target model and use the predictions obtained to train a clone model. Such a clone model learns to approximate the decision boundary of the target model, achieving high accuracy on in-distribution examples. We propose Ensemble of Diverse Models (EDM) to defend against such MS attacks. EDM is made up of models that are trained to produce dissimilar predictions for OOD inputs. By using a different member of the ensemble to service different queries, our defense produces predictions that are highly discontinuous in the input space for the adversary's OOD queries. Such discontinuities cause the clone model trained on these predictions to have poor generalization on in-distribution examples. Our evaluations on several image classification tasks demonstrate that EDM defense can severely degrade the accuracy of clone models (up to $39.7\%$). Our defense has minimal impact on the target accuracy, negligible computational costs during inference, and is compatible with existing defenses for MS attacks.
Dynamic Backdoor Attacks Against Deep Neural Networks
Authors: Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, Yang Zhang
Date: 2021-01-01 00:00:00

Current Deep Neural Network (DNN) backdooring attacks rely on adding static triggers (with fixed patterns and locations) on model inputs that are prone to detection.  In this paper, we propose the first class of dynamic backdooring techniques: Random Backdoor, Backdoor Generating Network (BaN), and conditional Backdoor Generating Network (c-BaN). Triggers generated by our techniques have random patterns and locations. In particular, BaN and c-BaN based on a novel generative network are the first two schemes that algorithmically generate triggers.  Moreover, c-BaN is the first conditional backdooring technique that given a target label, it can generate a target-specific trigger.  Both BaN and c-BaN are essentially a general framework which renders the adversary the flexibility for further customizing backdoor attacks. We extensively evaluate our techniques on three benchmark datasets and show that our techniques achieve almost perfect attack performance on backdoored data with a negligible utility loss. More importantly, our techniques can bypass state-of-the-art defense mechanisms.
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning
Authors: Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Date: 2021-01-01 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL). Current CTDE-based methods rely on restrictive decompositions of the centralized value function across agents, which decomposes the global Q-value into individual Q values to guide individuals' behaviours. However, such expected, i.e., risk-neutral, Q value decomposition is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Our main contributions are in three folds: (i) We first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution; (ii) We then propose a dynamic risk level predictor for CVaR calculation to handle the temporal nature of the stochastic outcomes during executions; (iii) We finally propose risk-sensitive Bellman equation along with Individual-Global-MAX (IGM) for MARL training. Empirically, we show that our method significantly outperforms state-of-the-art methods on many challenging StarCraft II tasks, demonstrating significantly enhanced coordination and high sample efficiency.
Robust Meta-learning with Noise via Eigen-Reptile
Authors: Dong Chen, Lingfei Wu, Siliang Tang, Fangli Xu, Juncheng Li, Chang Zong, Chilie Tan, Yueting Zhuang
Date: 2021-01-01 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Recent years have seen a surge of interest in meta-learning techniques for tackling the few-shot learning (FSL) problem. However, the meta-learner's initial model is prone to meta-overfit, as there are only a few available samples with sampling noise. Besides, when handling the data sampled with label noise for FSL, meta-learner could be extremely sensitive to label noise. To address these two challenges that FSL with sampling and label noise. In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step. We present Eigen-Reptile (ER) that updates the meta-parameters with the main direction of historical task-specific parameters to alleviate gradient noise. Specifically, the main direction is computed by a special mechanism for the parameter's large size. Furthermore, to obtain a more accurate main direction for Eigen-Reptile in the presence of label noise, we propose Introspective Self-paced Learning (ISPL) that constructs a plurality of prior models to determine which sample should be abandoned. We have proved the effectiveness of Eigen-Reptile and ISPL, respectively, theoretically and experimentally. Moreover, our experiments on different tasks demonstrate that the proposed methods outperform or achieve highly competitive performance compared with the state-of-the-art methods with or without noisy labels.