pwc-archive/papers-with-abstracts huggingface dataset:
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection
Authors: Jiaping Zhong, Weiying Xie, Jie Lei, Yunsong Li, Zan Li
Date: 2021-01-01 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network

Hyperspectral anomaly detection under high-dimensional data and interference of deteriorated bands without any prior information has been challenging and attracted close attention in the exploration of the unknown in real scenarios. However, some emerging methods based on generative adversarial network (GAN) suffer from the problems of gradient vanishing and training instability with struggling to strike a balance between performance and training sample limitations. In this work, aiming to remedy the drawbacks of existing methods, we present a novel multi-scale covariance map (MCM)-aware twin-least-square GAN (MTGAN). Instead of the widely used single-scale Gaussian hypothesis background estimation, in MTGAN, we introduce the MCM-aware strategy to construct multi-scale priors with precise second-order statistics, thereby implicitly bridging the spatial and spectral information. Thus, we reliably and adaptively represent the prior of HSI to change the priors-lack situation. Moreover, we impose the twin-least-square loss on GAN, which helps improve the generative ability and training stability in feature and image domains, overcoming the gradient vanishing problem. Finally, the network enforced with a new anomaly rejection loss establishes a pure and discriminative background estimation. Experiments demonstrate that the average detection accuracy of MTGAN reaches 0.99809, which is superior to the state-of-the-art algorithms.
Switching-Aligned-Words Data Augmentation for Neural Machine Translation
Authors: Fengshun Xiao, Zuchao Li, Hai Zhao
Date: 2021-01-01 00:00:00
Tasks: Data Augmentation, Machine Translation, NMT, Position, Translation

In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available. In this paper, we present a novel data augmentation method for neural machine translation by using only the original training data without extra data. More accurately, we randomly replace words or mixup with their aligned alternatives in another language when training neural machine translation models. Since aligned word pairs appear in the same position of each other during training, it is helpful to form bilingual embeddings which are proved useful to provide a performance boost \citep{liu2019shared}. Experiments on both small and large scale datasets show that our method significantly outperforms the baseline models.
A Text GAN for Language Generation with Non-Autoregressive Generator
Authors: Fei Huang, Jian Guan, Pei Ke, Qihan Guo, Xiaoyan Zhu, Minlie Huang
Date: 2021-01-01 00:00:00
Tasks: Decipherment, Representation Learning, Sentence, Text Generation

Despite the great success of Generative Adversarial Networks (GANs) in generating high-quality images, GANs for text generation still face two major challenges: first, most text GANs are unstable in training mainly due to ineffective optimization of the generator, and they heavily rely on maximum likelihood pretraining; second, most text GANs adopt autoregressive generators without latent variables, which largely limits the ability to learn latent representations for natural language text. In this paper, we propose a novel text GAN, named NAGAN, which incorporates a non-autoregressive generator with latent variables. The non-autoregressive generator can be effectively trained with gradient-based methods and free of pretraining. The latent variables facilitate representation learning for text generation applications. Experiments show that our model is competitive comparing with existing text GANs in unconditional text generation, and it outperforms existing methods on sentence manipulation in latent space and unsupervised text decipherment.
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning
Authors: Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang
Date: 2021-01-01 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adversarial imitation learning has emerged as a general and scalable framework for automatic reward acquisition. However, we point out that previous methods commonly exploited occupancy-dependent reward learning formulation. Despite the theoretical justification, the occupancy measures tend to cause issues in practice because of high variance and low vulnerability to domain shifts. Another reported problem is termination biases induced by provided rewarding and regularization schemes around terminal states. In order to deal with these issues, this work presents a novel algorithm called causal adversarial inverse reinforcement learning. The framework employs a dual discriminator architecture for decoupling state densities from rewards formulation. We investigate the reward shaping theory to deal with the finite horizon problem and address the reward function's optimality. The formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy. In experiments, we demonstrate that our approach outperforms prior methods in challenging continuous control tasks, even under significant variation in the environments.
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Authors: Yingjun Du, XianTong Zhen, Ling Shao, Cees G. M. Snoek
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Domain Generalization, Few-Shot Learning, Meta-Learning

Batch normalization plays a crucial role when training deep neural networks. However, batch statistics become unstable with small batch sizes and are unreliable in the presence of distribution shifts. We propose MetaNorm, a simple yet effective meta-learning normalization. It tackles the aforementioned issues in a unified way by leveraging the meta-learning setting and learns to infer adaptive statistics for batch normalization. MetaNorm is generic, flexible and model-agnostic, making it a simple plug-and-play module that is seamlessly embedded into existing meta-learning approaches. It can be efficiently implemented by lightweight hypernetworks with low computational cost. We verify its effectiveness by extensive evaluation on representative tasks suffering from the small batch and domain shift problems: few-shot learning and domain generalization. We further introduce an even more challenging setting: few-shot domain generalization. Results demonstrate that MetaNorm consistently achieves much better, or at least competitive, accuracy compared to existing batch normalization methods.
Optimizing Quantized Neural Networks with Natural Gradient
Authors: Jun Chen, Hanwen Chen, Jiangning Zhang, Wenzhou Chen, Yong liu, Yunliang Jiang
Date: 2021-01-01 00:00:00
Tasks: Computational Efficiency

Quantized Neural Networks (QNNs) have achieved an enormous step in improving computational efficiency, making it possible to deploy large models to mobile and miniaturized devices. In order to narrow the performance gap between low-precision and full-precision models, we introduce the natural gradient to train a low-precision model, which considers the curvature information of the model by viewing the parameter space as a Riemannian manifold. Specifically, we propose a novel Optimized Natural Gradient Descent (ONGD) method, which avoids the computation of Fisher Information Matrix (FIM) and updates the parameters with an amount of computation comparable to Stochastic Gradient Descent (SGD). We conduct an ablation study and results show that the 4-bit quantized ResNet-32 trained with ONGD has a better result than SGD, i.e. 2.05\% higher in Top-1 accuracy on CIFAR100 dataset.
Further comparison experiments illustrate that our method achieves state-of-the-art results in CIFAR and ImageNet datasets, where the 8-bit version of MobileNet achieves 0.25\%/0.13\% higher in Top-1/Top-5 accuracies than the full-precision version on ImageNet dataset.
Variational Multi-Task Learning
Authors: Jiayi Shen, XianTong Zhen, Marcel Worring, Ling Shao
Date: 2021-01-01 00:00:00
Tasks: Bayesian Inference, Inductive Bias, Multi-Task Learning

Multi-task learning aims to improve the overall performance of a set of tasks by leveraging their relatedness. When training data is limited using priors is pivotal, but currently this is done in ad-hoc ways. In this paper, we develop variational multi-task learning - VMTL, a general probabilistic inference framework for simultaneously learning multiple related tasks. We cast multi-task learning as a variational Bayesian inference problem, which enables task relatedness to be explored in a principled way by specifying priors. We introduce Gumbel-softmax priors to condition the prior of each task on related tasks. Each prior is represented as a mixture of variational posteriors of other related tasks and the mixing weights are learned in a data-driven manner for each individual task. The posteriors over representations and classifiers are inferred jointly for all tasks and individual tasks are able to improve their performance by using the shared inductive bias. Experimental results demonstrate that VMTL is able to tackle challenging multi-task learning with limited training data well, and it achieves state-of-the-art performance on three benchmarks, consistently surpassing previous methods.
Representation Balancing Offline Model-based Reinforcement Learning
Authors: Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: model, Model-based Reinforcement Learning, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the main challenges in offline and off-policy reinforcement learning is to cope with the distribution shift that arises from the mismatch between the target policy and the data collection policy. In this paper, we focus on a model-based approach, particularly on learning the representation for a robust model of the environment under the distribution shift, which has been first studied by Representation Balancing MDP (RepBM). Although this prior work has shown promising results, there are a number of shortcomings that still hinder its applicability to practical tasks. In particular, we address the curse of horizon exhibited by RepBM, rejecting most of the pre-collected data in long-term tasks. We present a new objective for model learning motivated by recent advances in the estimation of stationary distribution corrections. This effectively overcomes the aforementioned limitation of RepBM, as well as naturally extending to continuous action spaces and stochastic policies. We also present an offline model-based policy optimization using this new objective, yielding the state-of-the-art performance in a representative set of benchmark offline RL tasks.
FedMes: Speeding Up Federated Learning with Multiple Edge Servers
Authors: Dong-Jun Han, Minseok Choi, Jungwuk Park, Jaekyun Moon
Date: 2021-01-01 00:00:00
Tasks: Federated Learning

We consider federated learning with multiple wireless edge servers having their own local coverages. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the devices located in the overlapping areas between the coverage of edge servers; in the model-downloading stage, the devices in the overlapping areas receive multiple models from different edge servers, take the average of the received models, and then update the model with their local data. These devices send their updated model to multiple edge servers by broadcasting, which acts as bridges for sharing the trained models between servers. Even when some edge servers are given biased datasets within their coverages, their training processes can be assisted by coverages of adjacent servers, through the devices in the overlapping regions. As a result, the proposed scheme does not require costly communications with the central cloud server (located at the higher tier of edge servers) for model synchronization, significantly reducing the overall training time compared to the conventional cloud-based federated learning systems. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods.
Sself: Robust Federated Learning against Stragglers and Adversaries
Authors: Jungwuk Park, Dong-Jun Han, Minseok Choi, Jaekyun Moon
Date: 2021-01-01 00:00:00
Tasks: Data Poisoning, Federated Learning

While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. While the presence of both stragglers and adversaries raises serious concerns for the deployment of practical federated learning systems, no known schemes or known combinations of schemes, to our best knowledge, effectively address these two issues at the same time. In this work, we propose Sself, a semi-synchronous entropy and loss based filtering/averaging, to tackle both stragglers and adversaries simultaneously. The stragglers are handled by exploiting different staleness (arrival delay) information when combining locally updated models during periodic global aggregation. Various adversarial attacks are tackled by utilizing a small amount of public data collected at the server in each aggregation step, to first filter out the model-poisoned devices using computed entropies, and then perform weighted averaging based on the estimated losses to combat data poisoning and backdoor attacks. A theoretical convergence bound is established to provide insights on the convergence of Sself. Extensive experimental results show that Sself outperforms various combinations of existing methods aiming to handle stragglers/adversaries.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Authors: Dong Bok Lee, Dongchan Min, Seanie Lee, Sung Ju Hwang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Meta-Learning, Unsupervised Few-Shot Image Classification, Unsupervised Few-Shot Learning, Variational Inference

Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting.
Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples
Authors: Dylan Z Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
Date: 2021-01-01 00:00:00
Tasks: Clustering

With the greater proliferation of machine learning models, the imperative of diagnosing and correcting bugs in models has become increasingly clear. As a route to better discover and fix model bugs, we propose failure scenarios: regions on the data manifold that are incorrectly classified by a model. We propose an end-to-end debugging framework called Defuse to use these regions for fixing faulty classifier predictions. The Defuse framework works in three steps. First, Defuse identifies many unrestricted adversarial examples—naturally occurring instances that are misclassified—using a generative model. Next, the procedure distills the misclassified data using clustering. This step both reveals sources of model error and helps facilitate labeling. Last, the method corrects model behavior on the distilled scenarios through an optimization based approach. We illustrate the utility of our framework on a variety of image data sets. We find that Defuse identifies and resolves concerning predictions while maintaining model generalization.
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP
Authors: M Saiful Bari, Tasnim Mohiuddin, Shafiq Joty
Date: 2021-01-01 00:00:00
Tasks: Cross-Lingual Transfer, Data Augmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, NER, Paraphrase Identification, Self-Supervised Learning, Transfer Learning

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However,  annotated data for every target task in every target language is rare, especially for low-resource languages. We propose XLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios. In particular, XLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language task. At its core, XLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on zero-resource cross-lingual transfer tasks for Named Entity Recognition (NER), Natural Language Inference (NLI) and paraphrase identification on Paraphrase Adversaries from Word Scrambling (PAWS). XLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to XLA's success.
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
Authors: Hansen Wang, Zefang Zong, Tong Xia, Shuyu Luo, Meng Zheng, Depeng Jin, Yong Li
Date: 2021-01-01 00:00:00

The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers. It is of great importance to find an efficient and high-qualified solution. However, current algorithms of VRP including heuristics and RL based methods, only performs well on small-scale instances with usually no more than a hundred customers, and fails to solve large-scale VRP due to either high computation cost or the explosive exploration space that results in model divergence. Based on the classical idea of Divide-and-Conquer, we present a novel Rewriting-by-Generating(RBG) framework with hierarchical RL agents to solve the large-scale VRP. RBG contains a rewriter agent that refines the customer division and an elementary generator to generate regional solutions individually. Extensive experiments demonstrate our RBG framework has significant performance on large-scale VRP. RBG outperforms LKH3, the state-of-the-art method in solving VRP, by 2.43% with the problem size of N=2000 and could infer solutions about 100 times faster.
Self-supervised representation learning via adaptive hard-positive mining
Authors: Shaofeng Zhang, Junchi Yan, Xiaokang Yang
Date: 2021-01-01 00:00:00
Tasks: Contrastive Learning, Representation Learning, Self-Supervised Learning

Despite their success in perception over the last decade, deep neural networks are also known ravenous to labeled data for training, which limits their applicability to real-world problems. Hence self-supervised learning has recently attracted intensive attention. Contrastive learning has been one of the dominant approaches for effective feature extraction and achieve state-of-the-art performance. In this paper, we first theoretically show that these methods cannot fully take advantage of training samples in the sense of hard positive samples mining. Then we propose a new contrastive method called AdpCLR$^{full}$ (adaptive self-supervised contrastive learning representations), which can more effectively (supported by our proof) explore the samples in a way of being closer to supervised contrastive learning.  We thoroughly evaluate the quality of the learned representation on ImageNet for both pretraining based version (AdpCLR$^{pre}$) and fully trained version (AdpCLR$^{full}$). The results of accuracy show AdpCLR$^{pre}$ outperforms state-of-the-art contrastive-based models by 3.0\% with extra 100 epochs, while AdpCLR$^{full}$ outperforms by 2.5\% with additional 600 epochs.
Efficiently Troubleshooting Image Segmentation Models with Human-In-The-Loop
Authors: Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma
Date: 2021-01-01 00:00:00
Tasks: Autonomous Driving, Image Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis. It is, therefore, of great importance to not only improve the accuracy of segmentation models on well-established benchmarks, but also enhance their robustness in the real world so as to avoid sparse but fatal failures. In this paper, instead of chasing state-of-the-art performance on existing benchmarks, we turn our attention to a new challenging problem: how to efficiently expose failures of ``top-performing'' segmentation models in the real world and how to leverage such counterexamples to rectify the models. To achieve this with minimal human labelling effort, we first automatically sample a small set of images that are likely to falsify the target model from a large corpus of web images via the maximum discrepancy competition principle. We then propose a weakly labelling strategy to further reduce the number of false positives, before time-consuming pixel-level labelling by humans. Finally, we fine-tune the model to harness the identified failures, and repeat the whole process, resulting in an efficient and progressive framework for troubleshooting segmentation models. We demonstrate the feasibility of our framework using the semantic segmentation task in PASCAL VOC, and find that the fine-tuned model exhibits significantly improved generalization when applied to real-world images with greater content diversity. All experimental codes will be publicly released upon acceptance.