pwc-archive/papers-with-abstracts huggingface dataset:
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation
Authors: Linfeng Zhang, Kaisheng Ma
Date: 2021-09-29 00:00:00
Tasks: All, Contrastive Learning, Image-to-Image Translation, Knowledge Distillation, Translation

Recent progress in image-to-image translation has witnessed the success of generative adversarial networks (GANs). However, GANs usually contain a huge number of parameters, which lead to intolerant memory and computation consumption and limit their deployment on edge devices. To address this issue, knowledge distillation is proposed to transfer the knowledge learned by a cumbersome teacher model to an efficient student model. However, previous knowledge distillation methods directly train the student to learn teacher knowledge in all the spatial regions of the images but ignore the fact that in image-to-image translation a large number of regions (e.g. background regions) should not be translated and teacher features in these regions are not worthy to be distilled. To tackle this challenge,  in this paper, we propose Region-aware Knowledge Distillation which first localizes the crucial regions in the images with attention mechanism. Then, teacher features in these crucial regions are distilled to students with a region-wise contrastive learning framework. Besides distilling teacher knowledge in features, we further introduce perceptual distillation to distill teacher knowledge in the generated images. Experiments with four comparison methods demonstrate the substantial effectiveness of our method on both paired and unpaired image-to-image translation. For instance, our 7.08X compressed and 6.80X accelerated CycleGAN student outperforms its teacher by 1.36 and 1.16 FID scores on Horse to Zebra and Zebra to Horse, respectively. Codes have been released in the supplementary material and will be released on GitHub soon.
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
Authors: Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jian Shen, Jianye Hao, Yong Yu, Jun Wang
Date: 2021-09-29 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Imitation Learning, Reinforcement Learning (RL)

State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
However, previous methods attempt to learn the implicit state-to-action mapping policy directly from state-only data, which results in ambiguity and inefficiency.
In this paper, we overcome this issue by introducing hyper-policy as sets of policies that share the same state transition to characterize the optimality in SOIL. Accordingly, we propose Decoupled Policy Optimization (DPO) via explicitly decoupling the state-to-action mapping policy as a state transition predictor and an inverse dynamics model. Intuitively, we teach the agent to plan the target to go and then learn its own skills to reach. 
Experiments on standard benchmarks and a real-world driving dataset demonstrate the effectiveness of DPO and its potential of bridging the gap between reality and simulations of reinforcement learning.
Improving the Accuracy of Learning Example Weights for Imbalance Classification
Authors: Yuqi Liu, Bin Cao, Jing Fan
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Classification, image-classification, Image Classification, Multi-class Classification

To solve the imbalance classification, methods of weighting examples haven been proposed. Recent work has studied to assign adaptive weights to training examples through learning mechanisms, that is, the weights, similar to classification models, are regarded as parameters that need to be learned. However, the algorithms in recent work use local information to approximately optimize the weights, which may lead to inaccurate learning of the weights. In this work, we first propose a novel mechanism of learning with a constraint, which can accurately train the weights and model. Then, we propose a combined method of our learning mechanism and the work by Hu et al., which can promote each other to perform better. Our proposed method can be applied to any type of deep network model. Experiments show that compared with the state-of-art algorithms, our method has significant improvement in varieties of settings, including text and image classification over different imbalance ratios, binary and multi-class classification.
Fast Generic Interaction Detection for Model Interpretability and Compression
Authors: Tianjian Zhang, Feng Yin, Zhi-Quan Luo
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Deep Learning, scientific discovery

The ability of discovering feature interactions in a black-box model is vital to explainable deep learning. We propose a principled, global interaction detection method by casting our target as a multi-arm bandits problem and solving it swiftly with the UCB algorithm. This adaptive method is free of ad-hoc assumptions and among the cutting-edge methods with outstanding detection accuracy and stability. Based on the detection outcome, a lightweight and interpretable deep learning model (called ParaACE) is further built using the alternating conditional expectation (ACE) method. Our proposed ParaACE improves the prediction performance by 26 % and reduces the model size by 100+ times as compared to its Teacher model over various datasets. Furthermore, we show the great potential of our method for scientific discovery through interpreting various real datasets in the economics and smart medicine sectors. The code is available at http://github.com/xxx.
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer
Authors: Chenyu Liu, Jia Li, Junxian Duan, Huaibo Huang
Date: 2021-09-29 00:00:00
Tasks: Face Swapping, Optical Flow Estimation

The rapid development of video processing technology makes it easy for people to forge videos without leaving visual artifacts. The spread of forged videos may lead to moral and legal consequences and pose a potential threat to people's lives and social stability. So it is significant to identify deepfake video information. Although the previous detection methods have achieved high accuracy, the generalization is poor when facing unprecedented data in the real scene. There are three fundamental reasons. The first is that capturing the general clue of artifacts is difficult. The second is that selecting the appropriate model is challenging in specific feature extraction. The third is that exploiting fully and effectively the extracted features is hard. We find that the high-frequency information in the image and the texture in the shallow layer of the model expose the subtle artifacts. The optical flow of the real video has variations while the optical flow of the deepfake video has rarely variations. Furthermore, consecutive frames in the real video have temporal consistency. In this paper, we propose a dual-branch video forgery detection model named ENST, which integrates parallelly and interactively EfficientNet-B5 and Swin Transformer. Specifically, EfficientNet-B5 extracts the artifacts information of high frequency and texture in the shallow layer of the model. Swin Transformer captures the subtle discrepancies between optical flows. To extract more robust face features, we design a new loss function for EfficientNet-B5. In addition, we also introduce the attention mechanism into EfficientNet-B5 to enhance the extracted features. We conduct test experiments on FaceForensics++ and Celeb-DF (v2) datasets, and comprehensive results show that ENST has higher accuracy and generalization, which is superior to the most advanced methods.
Time Delay Estimation of Traffic Congestion Based on Statistical Causality
Authors: YongKyung Oh, Ji-In Kwak, JuYeong Lee, Sungil Kim
Date: 2021-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

Considering how congestion will propagate in the near future, understanding traffic congestion propagation has become crucial in GPS navigation systems for providing users with a more accurate estimated time of arrival (ETA). However, providing the exact ETA during congestion is a challenge owing to the complex propagation process between roads and high uncertainty regarding the future behavior of the process. To aid in accurate ETA calculation during congestion, we propose a novel time delay estimation method for the propagation of traffic congestion due to traffic accidents using lag-specific transfer entropy (TE). Nonlinear normalization with a sliding window is used to effectively reveal the causal relationship between the source and target time series in calculating the TE. Moreover, Markov bootstrap techniques were adopted to quantify the uncertainty in the time delay estimator. To the best of our knowledge, the proposed method is the first to estimate the time delay based on the causal relationship between adjacent roads. The proposed method was validated using simulated data as well as real user trajectory data obtained from a major GPS navigation system applied in South Korea.
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks
Authors: Fan Wang, Hao Tian, Haoyi Xiong, Hua Wu, Yang Cao, Yu Kang, Haifeng Wang
Date: 2021-09-29 00:00:00
Tasks: Meta-Learning

While artificial neural networks (ANNs) have been widely adopted in machine learning, researchers are increasingly obsessed by the gaps between ANNs and natural neural networks (NNNs). In this paper, we propose a framework named as Evolutionary Plastic Recurrent Neural Networks (EPRNN). Inspired by NNN, EPRNN composes Evolution Strategies, Plasticity Rules, and Recursion-based Learning all in one meta learning framework for generalization to different tasks. More specifically, EPRNN incorporates with nested loops for meta learning --- an outer loop searches for optimal initial parameters of the neural network and learning rules; an inner loop adapts to specific tasks. In the inner loop of EPRNN, we effectively attain both long term memory and short term memory by forging plasticity with recursion-based learning mechanisms, both of which are believed to be responsible for the formation of memories in NNNs.  The inner-loop setting closely simulate that of NNNs, which neither query from any gradient oracle for optimization nor require the exact forms of learning objectives. To evaluate the performance of EPRNN, we carry out extensive experiments in two groups of tasks:  Sequence Predicting, and Wheeled Robot Navigating. The experiment results demonstrate the unique advantage of EPRNN compared to state-of-the-arts based on plasticity and recursion while yielding comparably good performance against deep learning based approaches in the tasks. The experiment results suggest the potential of EPRNN to generalize to variety of tasks and encourage more efforts in plasticity and recursion based learning mechanisms.
Interpreting Reinforcement Policies through Local Behaviors
Authors: Ronny Luss, Amit Dhurandhar, Miao Liu
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this paper, we propose a novel perspective to understanding RL policies based on identifying important states from automatically learned meta-states. The key conceptual difference between our approach and many previous ones is that we form meta-states based on locality governed by the expert policy dynamics rather than based on similarity of actions, and that we do not assume any particular knowledge of the underlying topology of the state space. Theoretically, we show that our algorithm to find meta-states converges and the objective that selects important states from each meta-state is submodular leading to efficient high quality greedy selection. Experiments on three domains (four rooms, door-key and minipacman) and a carefully conducted user study illustrate that our perspective leads to better understanding of the policy. We conjecture that this is a result of our meta-states being more intuitive in that the corresponding important states are strong indicators of tractable intermediate goals that are easier for humans to interpret and follow.
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning
Authors: Satheesh K Perepu, Kaushik Dey
Date: 2021-09-29 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Multi-Agent reinforcement learning has received lot of attention in recent years and have applications in many different areas. Existing methods involving Centralized Training and Decentralized execution, attempts to train the agents towards learning a pattern of coordinated actions to arrive at optimal joint policy. However if some agents are stochastic in their action to varying degrees, the above methods provides poor coordination among agents. In this paper we show how the stochasticity of agents, which could be a result of malfunction  or aging of robots, can add to the uncertainty in coordination and thereby contribute to unsatisfactory global rewards. In such a scenario, the deterministic agents have to understand the behavior and limitations of the  stochastic agents while the stochastic agents have to plan taking in cognizance their own limitations. Our proposed method, Deep Stochastic Discounted Factor (DSDF), tunes the discounted factor for the agents by using a learning representation of uncertainty to update the utility networks of individual agents. DSDF also helps in imparting an extent of reliability in coordination thereby granting stochastic agents tasks which are immediate and of shorter trajectory with deterministic ones taking the tasks which involve longer planning. Results on benchmark environments shows the efficacy of the proposed approach when compared with existing deterministic approaches.
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management
Authors: Saeed Marzban, Erick Delage, Jonathan Li
Date: 2021-09-29 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning (RL)

The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be made over time with the consideration of many factors such as investors’ preferences, trading environment, and market conditions. In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL) that can exploit more effectively cross-asset dependency information and achieve better performance than  state-of-the-art architectures. In doing so, we introduce a new form of permutation invariance property for policy networks and derive general theory for verifying its applicability. Our portfolio policy network, named WaveCorr, is the first convolutional neural network architecture that preserves this invariance property when treating asset correlation information. Finally, in a set of experiments conducted using data from both Canadian (TSX) and American stock markets (S\&P 500), WaveCorr consistently outperforms other architectures with an impressive 3\%-25\% absolute improvement in terms of average annual return, and up to more than 200\% relative improvement in average Sharpe ratio. We also measured an improvement of a factor of up to 5 in the stability of performance under random choices of initial asset ordering and weights. The stability of the network has been found as particularly valuable by our industrial partner.
ENHANCE THE DYNAMIC REGRET VIA OPTIMISM
Authors: Qing-xin Meng, Jian-wei Liu
Date: 2021-09-29 00:00:00

In this paper, we study the enhancement method for dynamic regret in online convex optimization. Existing works have shown that adaptive learning for dynamic environment (Ader) enjoys an $O\big(\sqrt{\left(1+P_T\right)T}\,\big)$ dynamic regret upper bound, where $T$ is the number of rounds and $P_T$ is the path length of the reference strategy sequence. The basic idea of Ader is to maintain a group of experts, where each expert obtains the best dynamic regret of a specific path length by running Mirror Descent (MD) with specific parameter, and then tracks the best expert by Normalized Exponentiated Subgradient (NES). However, Ader is not environmental adaptive. By introducing the estimated linear loss function $\widehat{x}_{t}^*$, the dynamic regret for Optimistic Mirror Descent (OMD) is tighter than MD if the environment is not completely adversarial and $\widehat{x}_{t}^*$ is well-estimated. Based on the fact that optimism can enhance dynamic regret, we develop an algorithm to replace MD and NES in Ader with OMD and Optimistic Normalized Exponentiated Subgradient (ONES) respectively, and utilize the adaptive trick to achieve $O\big(\sqrt {\left(1+P_T\right)M_T}\,\big)$ dynamic regret upper bound, where $M_T\leqslant O\left(T\right)$ is a measure of estimation accuracy. In particular, if $\widehat{x}_t^*\in\partial\widehat{\varphi}_t$, where $\widehat{\varphi}_t$ represents the estimated convex loss function and $\partial\widehat{\varphi}_t$ is Lipschitz continuous, then the dynamic regret upper bound of OMD has a subgradient variation type. Based on this fact, we develop a variant algorithm whose upper bound has a subgradient variation type. All our algorithms are environmental adaptive.
Beyond Quantization: Power aware neural networks
Authors: Nurit Spingarn, Elad Hoffer, Ron Banner, Hilla Ben Yaacov, Tomer Michaeli
Date: 2021-09-29 00:00:00
Tasks: Quantization

Power consumption is a major obstacle in the deployment of deep neural networks (DNNs) on end devices. Existing approaches for reducing power consumption rely on quite general principles, including avoidance of multiplication operations and aggressive quantization of weights and activations. However, these methods do not take into account the precise power consumed by each module in the network, and are therefore far from optimal. In this paper we develop accurate power consumption models for all arithmetic operations in the DNN, under various working conditions. Surprisingly, we reveal several important factors that have been overlooked to date. Based on our analysis, we present PANN (power-aware neural network), a simple approach for approximating any full-precision network by a low-power fixed-precision variant. Our method can be applied to a pre-trained network, and can also be used during training to achieve improved performance. In contrast to previous approaches, our method incurs only a minor degradation in accuracy w.r.t. the full-precision version of the network, even when working at the power-budget of a 2-bit quantized variant. In addition, our scheme enables to seamlessly traverse the power-accuracy tradeoff at deployment time, which is a major advantage over existing quantization methods that are constrained to specific bit widths.
Neural Latent Traversal with Semantic Constraints
Authors: Majid Abdolshah, Hung Le, Thommen Karimpanal George, Vuong Le, Sunil Gupta, Santu Rana, Svetha Venkatesh
Date: 2021-09-29 00:00:00

Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images. Recent approaches proposed to learn linear models to relate the latent codes with the attributes to enable adjustment of the attributes. However, as the latent spaces of GANs are learnt in an unsupervised manner and are semantically entangled, the linear models are not always effective. In this study, we learn multi-stage neural transformations of latent spaces of pre-trained GANs that enable more accurate modeling of the relation between the latent codes and the semantic attributes. To ensure identity preservation of images, we propose a sparsity constraint on the latent space transformations that is guided by the mutual information between the latent and the semantic space. We demonstrate our method on two face datasets (FFHQ and CelebA-HQ) and show that it outperforms current state-of-the-art baselines based on FID score and other numerical metrics.
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity
Authors: Zhe Wang, Jie Lin, Xue Geng, Mohamed M. Sabry Aly, Vijay Chandrasekhar
Date: 2021-09-29 00:00:00
Tasks: CPU, Quantization

Allocating different bit widths to different channels and quantizing them independently bring higher quantization precision and accuracy. Most of prior works use equal bit width to quantize all layers or channels, which is sub-optimal. On the other hand, it is very challenging to explore the hyperparameter space of channel bit widths, as the search space increases exponentially as the number of channels, which could be tens of thousand in a deep neural network. In this paper, we address an important problem of efficiently exploring the hyperparameter space of channel bit widths. We formulate the quantization of deep neural networks as a rate-distortion optimization problem, and present an ultra-fast algorithm to search the bit allocation of channels. Our approach has only linear time complexity and can find the optimal bit allocation within a few minutes on CPU. In addition, we provide an effective way to improve the performance on target hardware platforms. We restrict the bit rate (size) of each layer to allow as many weights and activations as possible to be stored on-chip, and incorporate hardware-aware constraints into our objective function. The hardware-aware constraints do not cause additional overhead to optimization, and have very positive impact on hardware performance. Experimental results show that our approach achieves state-of-the-art quantization results on four deep neural networks, ResNet-18, ResNet-34, ResNet-50, and MobileNet-v2, on ImageNet. Hardware simulation results demonstrate that our approach is able to bring up to 3.5x and 3.0x speedup on two deep-learning accelerators, TPU and Eyeriss, respectively.
GIR Framework: Learning Graph Positional Embeddings with Anchor Indication and Path Encoding
Authors: Yuheng Lu, Jinpeng Chen, Chuxiong Sun, Jie Hu
Date: 2021-09-29 00:00:00
Tasks: Position

The majority of existing graph neural networks (GNNs) following the message passing neural network (MPNN) pattern have limited power in capturing position information for a given node. To solve such problems, recent works exploit positioning nodes with selected anchors, mostly in a process that first explicitly assign distances information and then perform message passing encoding. However, this two-stage strategy may ignore potentially useful interaction between intermediate results of the distance computing and encoding stages. In this work, we propose a novel framework which follows the anchor-based idea and aims at conveying distance information implicitly along the MPNN message passing steps for encoding position information, node attributes, and graph structure in a more flexible way. Specifically, we first leverage a simple anchor indication strategy to enable the position-aware ability for well-designed MPNNs. Then, following this strategy, we propose the Graph Inference Representation (GIR) model, which acts as a generalization of MPNNs with a more specific propagation path design for position-aware scenarios.  Meanwhile, we theoretically and empirically explore the ability of the proposed framework to get position-aware embeddings, and experimental results show that our proposed method generally outperforms previous position-aware GNN methods.