pwc-archive/papers-with-abstracts huggingface dataset:
Visual Attack and Defense on Text
Authors: Shengjun Liu, Ningkang Jiang, Yuanbin Wu
Date: 2020-08-07 00:00:00
Tasks: Adversarial Attack

Modifying characters of a piece of text to their visual similar ones often ap-pear in spam in order to fool inspection systems and other conditions, which we regard as a kind of adversarial attack to neural models. We pro-pose a way of generating such visual text attack and show that the attacked text are readable by humans but mislead a neural classifier greatly. We ap-ply a vision-based model and adversarial training to defense the attack without losing the ability to understand normal text. Our results also show that visual attack is extremely sophisticated and diverse, more work needs to be done to solve this.
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications
Authors: Yunhai Han, YuHan Liu, David Paz, Henrik Christensen
Date: 2020-10-14 00:00:00
Tasks: Autonomous Driving

Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is presented. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance.
Self-Supervised Domain Adaptation with Consistency Training
Authors: L. Xiao, J. Xu, D. Zhao, Z. Wang, L. Wang, Y. Nie, B. Dai
Date: 2020-10-15 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Representation Learning, Unsupervised Domain Adaptation

We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. However, the obtained feature representation may contain a large amount of irrelevant information with respect to the main task. To provide further guidance, we force the feature representation of the augmented data to be consistent with that of the original data. Intuitively, the consistency introduces additional constraints to representation learning, therefore, the learned representation is more likely to focus on the right information about the main task. Our experimental results validate the proposed method and demonstrate state-of-the-art performance on classical domain adaptation benchmarks. Code is available at https://github.com/Jiaolong/ss-da-consistency.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Authors: Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang
Date: 2020-10-15 00:00:00
Proceeding: NAACL 2021 4
Tasks: Language Modeling, Language Modelling, Sentence, Sentence-Pair Classification, Sentiment Analysis, Text Classification, Word Sense Disambiguation

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, this contrastive self-training framework can gradually improve model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins on 7 benchmarks in 6 tasks, and achieves competitive performance with fully-supervised fine-tuning methods.
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification
Authors: Benlei Cui, XueMei Dong, Qiaoqiao Zhan, Jiangtao Peng, Weiwei Sun
Date: 2020-10-15 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Deep learning methods have shown considerable potential for hyperspectral image (HSI) classification, which can achieve high accuracy compared with traditional methods. However, they often need a large number of training samples and have a lot of parameters and high computational overhead. To solve these problems, this paper proposes a new network architecture, LiteDepthwiseNet, for HSI classification. Based on 3D depthwise convolution, LiteDepthwiseNet can decompose standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters. Moreover, we remove the ReLU layer and Batch Normalization layer in the original 3D depthwise convolution, which significantly improves the overfitting phenomenon of the model on small sized datasets. In addition, focal loss is used as the loss function to improve the model's attention on difficult samples and unbalanced data, and its training performance is significantly better than that of cross-entropy loss or balanced cross-entropy loss. Experiment results on three benchmark hyperspectral datasets show that LiteDepthwiseNet achieves state-of-the-art performance with a very small number of parameters and low computational cost.
Understanding Neural Abstractive Summarization Models via Uncertainty
Authors: Jiacheng Xu, Shrey Desai, Greg Durrett
Date: 2020-10-15 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Abstractive Text Summarization, Decoder, Sentence, Text Generation

An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model's token-level predictions. For two strong pre-trained models, PEGASUS and BART on two summarization datasets, we find a strong correlation between low prediction entropy and where the model copies tokens rather than generating novel text. The decoder's uncertainty also connects to factors like sentence position and syntactic distance between adjacent pairs of tokens, giving a sense of what factors make a context particularly selective for the model's next output token. Finally, we study the relationship of decoder uncertainty and attention behavior to understand how attention gives rise to these observed effects in the model. We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly.
Diverse Keyphrase Generation with Neural Unlikelihood Training
Authors: Hareesh Bahuleyan, Layla El Asri
Date: 2020-10-15 00:00:00
Proceeding: COLING 2020 8
Tasks: Diversity, Keyphrase Generation, Text Generation

In this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, demonstrated through improvements on quality metrics such as F1-score. However, the importance of diversity in keyphrase generation has been largely ignored. We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE). Our findings show that repetition of keyphrases is a major issue with MLE training. To alleviate this issue, we adopt neural unlikelihood (UL) objective for training the S2S model. Our version of UL training operates at (1) the target token level to discourage the generation of repeating tokens; (2) the copy token level to avoid copying repetitive tokens from the source text. Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well. Through extensive experiments on datasets from three different domains we demonstrate that the proposed approach attains considerably large diversity gains, while maintaining competitive output quality.
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Authors: Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha Dvornek, Xenophon Papademetris, James S. Duncan
Date: 2020-10-15 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling

Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). For many models such as convolutional neural networks (CNNs), adaptive methods typically converge faster but generalize worse compared to SGD; for complex settings such as generative adversarial networks (GANs), adaptive methods are typically the default because of their stability.We propose AdaBelief to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability. The intuition for AdaBelief is to adapt the stepsize according to the "belief" in the current gradient direction. Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step. We validate AdaBelief in extensive experiments, showing that it outperforms other methods with fast convergence and high accuracy on image classification and language modeling. Specifically, on ImageNet, AdaBelief achieves comparable accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief demonstrates high stability and improves the quality of generated samples compared to a well-tuned Adam optimizer. Code is available at https://github.com/juntang-zhuang/Adabelief-Optimizer
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
Authors: Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Date: 2020-10-15 00:00:00
Tasks: BIG-bench Machine Learning, Feature Importance, Interpretable Machine Learning

Explainable AI is an emerging field providing solutions for acquiring insights into automated systems' rationale. It has been put on the AI map by suggesting ways to tackle key ethical and societal issues. Existing explanation techniques are often not comprehensible to the end user. Lack of evaluation and selection criteria also makes it difficult for the end user to choose the most suitable technique. In this study, we combine logic-based argumentation with Interpretable Machine Learning, introducing a preliminary meta-explanation methodology that identifies the truthful parts of feature importance oriented interpretations. This approach, in addition to being used as a meta-explanation technique, can be used as an evaluation or selection tool for multiple feature importance techniques. Experimentation strongly indicates that an ensemble of multiple interpretation techniques yields considerably more truthful explanations.
Masked Contrastive Representation Learning for Reinforcement Learning
Authors: Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Houqiang Li
Date: 2020-10-15 00:00:00
Tasks: Atari Games, Contrastive Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Improving sample efficiency is a key research problem in reinforcement learning (RL), and CURL, which uses contrastive learning to extract high-level features from raw pixels of individual video frames, is an efficient algorithm~\citep{srinivas2020curl}. We observe that consecutive video frames in a game are highly correlated but CURL deals with them independently. To further improve data efficiency, we propose a new algorithm, masked contrastive representation learning for RL, that takes the correlation among consecutive inputs into consideration. In addition to the CNN encoder and the policy network in CURL, our method introduces an auxiliary Transformer module to leverage the correlations among video frames. During training, we randomly mask the features of several frames, and use the CNN encoder and Transformer to reconstruct them based on the context frames. The CNN encoder and Transformer are jointly trained via contrastive learning where the reconstructed features should be similar to the ground-truth ones while dissimilar to others. During inference, the CNN encoder and the policy network are used to take actions, and the Transformer module is discarded. Our method achieves consistent improvements over CURL on $14$ out of $16$ environments from DMControl suite and $21$ out of $26$ environments from Atari 2600 Games. The code is available at https://github.com/teslacool/m-curl.
Interactive Latent Interpolation on MNIST Dataset
Authors: Mazeyar Moeini Feizabadi, Ali Mohammed Shujjat, Sarah Shahid, Zainab Hasnain
Date: 2020-10-15 00:00:00
Tasks: Dimensionality Reduction

This paper will discuss the potential of dimensionality reduction with a web-based use of GANs. Throughout a variety of experiments, we show synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with latent vectors. GANs have proved to be a remarkable technique to produce computer-generated images, very similar to an original image. This is primarily useful when coupled with dimensionality reduction as an effective application of our algorithm. We proposed a new architecture for GANs, which ended up not working for mathematical reasons later explained. We then proposed a new web-based GAN that still takes advantage of dimensionality reduction to speed generation in the browser to .2 milliseconds. Lastly, we made a modern UI with linear interpolation to present the work. With the speedy generation, we can generate so fast that we can create an animation type effect that we have never seen before that works on both web and mobile.
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Authors: Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung
Date: 2020-10-14 00:00:00
Tasks: Adversarial Attack, text-classification, Text Classification

Training robust deep learning models for down-stream tasks is a critical challenge. Research has shown that down-stream models can be easily fooled with adversarial inputs that look like the training data, but slightly perturbed, in a way imperceptible to humans. Understanding the behavior of natural language models under these attacks is crucial to better defend these models against such attacks. In the black-box attack setting, where no access to model parameters is available, the attacker can only query the output information from the targeted model to craft a successful attack. Current black-box state-of-the-art models are costly in both computational complexity and number of queries needed to craft successful adversarial examples. For real world scenarios, the number of queries is critical, where less queries are desired to avoid suspicion towards an attacking agent. In this paper, we propose Explain2Attack, a black-box adversarial attack on text classification task. Instead of searching for important words to be perturbed by querying the target model, Explain2Attack employs an interpretable substitute model from a similar domain to learn word importance scores. We show that our framework either achieves or out-performs attack rates of the state-of-the-art models, yet with lower queries cost and higher efficiency.
LTN: Long-Term Network for Long-Term Motion Prediction
Authors: YingQiao Wang
Date: 2020-10-15 00:00:00
Tasks: Autonomous Navigation, motion prediction, Prediction, regression, Trajectory Prediction

Making accurate motion prediction of surrounding agents such as pedestrians and vehicles is a critical task when robots are trying to perform autonomous navigation tasks. Recent research on multi-modal trajectory prediction, including regression and classification approaches, perform very well at short-term prediction. However, when it comes to long-term prediction, most Long Short-Term Memory (LSTM) based models tend to diverge far away from the ground truth. Therefore, in this work, we present a two-stage framework for long-term trajectory prediction, which is named as Long-Term Network (LTN). Our Long-Term Network integrates both the regression and classification approaches. We first generate a set of proposed trajectories with our proposed distribution using a Conditional Variational Autoencoder (CVAE), and then classify them with binary labels, and output the trajectories with the highest score. We demonstrate our Long-Term Network's performance with experiments on two real-world pedestrian datasets: ETH/UCY, Stanford Drone Dataset (SDD), and one challenging real-world driving forecasting dataset: nuScenes. The results show that our method outperforms multiple state-of-the-art approaches in long-term trajectory prediction in terms of accuracy.
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Authors: Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai
Date: 2020-10-15 00:00:00
Proceeding: ICLR 2021 1
Tasks: Semantic Segmentation

Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse scenarios. Despite the success of the widely adopted cross-entropy loss and its variants, the mis-alignment between the loss functions and evaluation metrics degrades the network performance. Meanwhile, manually designing loss functions for each specific metric requires expertise and significant manpower. In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric. We substitute the non-differentiable operations in the metrics with parameterized functions, and conduct parameter search to optimize the shape of loss surfaces. Two constraints are introduced to regularize the search space and make the search efficient. Extensive experiments on PASCAL VOC and Cityscapes demonstrate that the searched surrogate losses outperform the manually designed loss functions consistently. The searched losses can generalize well to other datasets and networks. Code shall be released.
Representation Learning via Invariant Causal Mechanisms
Authors: Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing, Charles Blundell
Date: 2020-10-15 00:00:00
Tasks: Contrastive Learning, Out-of-Distribution Generalization, Representation Learning, Self-Supervised Image Classification, Self-Supervised Learning

Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of this success remains limited. In this paper we analyze self-supervised representation learning using a causal framework. We show how data augmentations can be more effectively utilized through explicit invariance constraints on the proxy classifiers employed during pretraining. Based on this, we propose a novel self-supervised objective, Representation Learning via Invariant Causal Mechanisms (ReLIC), that enforces invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees. Further, using causality we generalize contrastive learning, a particular kind of self-supervised method, and provide an alternative theoretical explanation for the success of these methods. Empirically, ReLIC significantly outperforms competing methods in terms of robustness and out-of-distribution generalization on ImageNet, while also significantly outperforming these methods on Atari achieving above human-level performance on $51$ out of $57$ games.
Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Authors: Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, Sepp Hochreiter
Date: 2020-10-15 00:00:00
Tasks: Prediction

Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a life-saving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning hard and computationally expensive. In this study, we propose two Multi-Timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a single temporal resolution and branch out into each individual timescale for more recent input steps. We test these models on 516 basins across the continental United States and benchmark against the US National Water Model. Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.
Multi-Agent Trust Region Policy Optimization
Authors: Hepeng Li, Haibo He
Date: 2020-10-15 00:00:00
Tasks: Multi-agent Reinforcement Learning, Privacy Preserving, reinforcement-learning, Reinforcement Learning (RL)

We extend trust region policy optimization (TRPO) to multi-agent reinforcement learning (MARL) problems. We show that the policy update of TRPO can be transformed into a distributed consensus optimization problem for multi-agent cases. By making a series of approximations to the consensus optimization model, we propose a decentralized MARL algorithm, which we call multi-agent TRPO (MATRPO). This algorithm can optimize distributed policies based on local observations and private rewards. The agents do not need to know observations, rewards, policies or value/action-value functions of other agents. The agents only share a likelihood ratio with their neighbors during the training process. The algorithm is fully decentralized and privacy-preserving. Our experiments on two cooperative games demonstrate its robust performance on complicated MARL tasks.