pwc-archive/papers-with-abstracts huggingface dataset:
Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation
Authors: Xin Yi, Bo Ma, Yulin Zhang, Longyao Liu, Jiahao Wu
Date: 2021-02-06 00:00:00
Tasks: Domain Adaptation, Image Dehazing, Vocal Bursts Valence Prediction

Caused by the difference of data distributions, intra-domain gap and inter-domain gap are widely present in image processing tasks. In the field of image dehazing, certain previous works have paid attention to the inter-domain gap between the synthetic domain and the real domain. However, those methods only establish the connection from the source domain to the target domain without taking into account the large distribution shift within the target domain (intra-domain gap). In this work, we propose a Two-Step Dehazing Network (TSDN) with an intra-domain adaptation and a constrained inter-domain adaptation. First, we subdivide the distributions within the synthetic domain into subsets and mine the optimal subset (easy samples) by loss-based supervision. To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning. Finally, we conduct the constrained inter-domain adaptation from the real domain to the optimal subset of the synthetic domain, alleviating the domain shift between domains as well as the distribution shift within the real domain. Extensive experimental results demonstrate that our framework performs favorably against the state-of-the-art algorithms both on the synthetic datasets and the real datasets.
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
Authors: Ziquan Liu, Yufei Cui, Jia Wan, Yu Mao, Antoni B. Chan
Date: 2021-02-06 00:00:00
Tasks: Crowd Counting, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

Weight decay is often used to ensure good generalization in the training practice of deep neural networks with batch normalization (BN-DNNs), where some convolution layers are invariant to weight rescaling due to the normalization. In this paper, we demonstrate that the practical usage of weight decay still has some unsolved problems in spite of existing theoretical work on explaining the effect of weight decay in BN-DNNs. On the one hand, when the non-adaptive learning rate e.g. SGD with momentum is used, the effective learning rate continues to increase even after the initial training stage, which leads to an overfitting effect in many neural architectures. On the other hand, in both SGDM and adaptive learning rate optimizers e.g. Adam, the effect of weight decay on generalization is quite sensitive to the hyperparameter. Thus, finding an optimal weight decay parameter requires extensive parameter searching. To address those weaknesses, we propose to regularize the weight norm using a simple yet effective weight rescaling (WRS) scheme as an alternative to weight decay. WRS controls the weight norm by explicitly rescaling it to the unit norm, which prevents a large increase to the gradient but also ensures a sufficiently large effective learning rate to improve generalization. On a variety of computer vision applications including image classification, object detection, semantic segmentation and crowd counting, we show the effectiveness and robustness of WRS compared with weight decay, implicit weight rescaling (weight standardization) and gradient projection (AdamP).
IC Networks: Remodeling the Basic Unit for Convolutional Neural Networks
Authors: Junyi An, Fengshan Liu, Jian Zhao, Furao Shen
Date: 2021-02-06 00:00:00

Convolutional neural network (CNN) is a class of artificial neural networks widely used in computer vision tasks. Most CNNs achieve excellent performance by stacking certain types of basic units. In addition to increasing the depth and width of the network, designing more effective basic units has become an important research topic. Inspired by the elastic collision model in physics, we present a general structure which can be integrated into the existing CNNs to improve their performance. We term it the "Inter-layer Collision" (IC) structure. Compared to the traditional convolution structure, the IC structure introduces nonlinearity and feature recalibration in the linear convolution operation, which can capture more fine-grained features. In addition, a new training method, namely weak logit distillation (WLD), is proposed to speed up the training of IC networks by extracting knowledge from pre-trained basic models. In the ImageNet experiment, we integrate the IC structure into ResNet-50 and reduce the top-1 error from 22.38% to 21.75%, which also catches up the top-1 error of ResNet-100 (21.75%) with nearly half of FLOPs.
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Authors: Yuxiao Lu, Jie Lin, Chao Jin, Zhe Wang, Min Wu, Khin Mi Mi Aung, XiaoLi Li
Date: 2021-02-06 00:00:00
Tasks: Privacy Preserving

Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Convolutional Neural Network (CNN) inference for privacy-preserving applications in clouds. To reduce the inference latency, one approach is to pack multiple messages into a single ciphertext in order to reduce the number of ciphertexts and support massive parallelism of Homomorphic Multiply-Accumulate (HMA) operations between ciphertexts. Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures. In this paper, we propose a low-rank factorization method named FFConv dedicated to efficient ciphertext packing for reducing both the rotation overhead and HMA operations. FFConv approximates a d x d convolution layer with low-rank factorized convolutions, in which a d x d low-rank convolution with fewer channels is followed by a 1 x 1 convolution to restore the channels. The d x d low-rank convolution with DensePack leads to significantly reduced rotation operations, while the rotation overhead of 1 x 1 convolution with ConvPack is close to zero. To our knowledge, FFConv is the first work that is capable of reducing the rotation overhead incurred by DensePack and ConvPack simultaneously, without introducing additional special blocks into the HECNN inference pipeline. Compared to prior art LoLa and Falcon, our method reduces the inference latency by up to 88% and 21%, respectively, with comparable accuracy on MNIST and CIFAR-10.
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
Authors: Haowei Sun, Shuo Feng, Xintao Yan, Henry X. Liu
Date: 2021-02-06 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
Authors: Jian Hu, Siyang Jiang, Seth Austin Harding, Haibin Wu, Shih-wei Liao
Date: 2021-02-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SMAC, SMAC+, Starcraft, Starcraft II

Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms. QMIX, a widely popular MARL algorithm, has been used to solve cooperative tasks, e.g. Starcraft Multi-Agent Challenge (SMAC), Difficulty-Enhanced Predator-Prey (DEPP). Recent variants of QMIX target relaxing the monotonicity constraint of QMIX, allowing for performance improvement in SMAC. However, in this paper, we investigate the code-level optimizations of these variants and the monotonicity constraint. We find that (1) such improvements of the variants are significantly affected by various code-level optimizations; (2) QMIX with normalized optimizations outperforms other previous works in SMAC; (3) the monotonicity constraint may improve sample efficiency in SMAC and DEPP. Last, a discussion with theoretical analysis is demonstrated about why QMIX works well in SMAC. We open-source the code at \url{https://github.com/hijkzzz/pymarl2}.
Overcoming Bias in Community Detection Evaluation
Authors: Jeancarlo Campos Leão, Alberto H. F. Laender, Pedro O. S. Vaz de Melo
Date: 2021-02-06 00:00:00
Tasks: Community Detection

Community detection is a key task to further understand the function and the structure of complex networks. Therefore, a strategy used to assess this task must be able to avoid biased and incorrect results that might invalidate further analyses or applications that rely on such communities. Two widely used strategies to assess this task are generally known as structural and functional. The structural strategy basically consists in detecting and assessing such communities by using multiple methods and structural metrics. On the other hand, the functional strategy might be used when ground truth data are available to assess the detected communities. However, the evaluation of communities based on such strategies is usually done in experimental configurations that are largely susceptible to biases, a situation that is inherent to algorithms, metrics and network data used in this task. Furthermore, such strategies are not systematically combined in a way that allows for the identification and mitigation of bias in the algorithms, metrics or network data to converge into more consistent results. In this context, the main contribution of this article is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we measure the quality of a community by applying the structural and functional strategies, and the combination of both, to obtain different pieces of evidence. Then, we consider the divergences and the consensus among the pieces of evidence to identify and overcome possible sources of bias in community detection algorithms, evaluation metrics, and network data. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach to obtain more consistent conclusions about the quality of the detected communities.
Sound Event Detection in Urban Audio With Single and Multi-Rate PCEN
Authors: Christopher Ick, Brian McFee
Date: 2021-02-06 00:00:00
Tasks: Event Detection, Sound Event Detection

Recent literature has demonstrated that the use of per-channel energy normalization (PCEN), has significant performance improvements over traditional log-scaled mel-frequency spectrograms in acoustic sound event detection (SED) in a multi-class setting with overlapping events. However, the configuration of PCEN's parameters is sensitive to the recording environment, the characteristics of the class of events of interest, and the presence of multiple overlapping events. This leads to improvements on a class-by-class basis, but poor cross-class performance. In this article, we experiment using PCEN spectrograms as an alternative method for SED in urban audio using the UrbanSED dataset, demonstrating per-class improvements based on parameter configuration. Furthermore, we address cross-class performance with PCEN using a novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED performance with MRPCEN, demonstrating improvements to cross-class performance compared to traditional single-rate PCEN.
Improving Model and Search for Computer Go
Authors: Tristan Cazenave
Date: 2021-02-06 00:00:00
Tasks: Deep Reinforcement Learning, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The standard for Deep Reinforcement Learning in games, following Alpha Zero, is to use residual networks and to increase the depth of the network to get better results. We propose to improve mobile networks as an alternative to residual networks and experimentally show the playing strength of the networks according to both their width and their depth. We also propose a generalization of the PUCT search algorithm that improves on PUCT.
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication
Authors: Stephan C. Meylan, Ruthe Foushee, Elika Bergelson, Roger P. Levy
Date: 2021-02-06 00:00:00
Tasks: Language Acquisition

How do adults understand children's speech? Children's productions over the course of language development often bear little resemblance to typical adult pronunciations, yet caregivers nonetheless reliably recover meaning from them. Here, we employ a suite of Bayesian models of spoken word recognition to understand how adults overcome the noisiness of child language, showing that communicative success between children and adults relies heavily on adult inferential processes. By evaluating competing models on phonetically-annotated corpora, we show that adults' recovered meanings are best predicted by prior expectations fitted specifically to the child language environment, rather than to typical adult-adult language. After quantifying the contribution of this "child-directed listening" over developmental time, we discuss the consequences for theories of language acquisition, as well as the implications for commonly-used methods for assessing children's linguistic proficiency.
BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices
Authors: Nael Fasfous, Manoj-Rohit Vemparala, Alexander Frickenstein, Lukas Frickenstein, Walter Stechele
Date: 2021-02-06 00:00:00
Tasks: Face Recognition, General Classification

Face masks have long been used in many areas of everyday life to protect against the inhalation of hazardous fumes and particles. They also offer an effective solution in healthcare for bi-directional protection against air-borne diseases. Wearing and positioning the mask correctly is essential for its function. Convolutional neural networks (CNNs) offer an excellent solution for face recognition and classification of correct mask wearing and positioning. In the context of the ongoing COVID-19 pandemic, such algorithms can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus. These application scenarios impose major challenges to the underlying compute platform. The inference hardware must be cheap, small and energy efficient, while providing sufficient memory and compute power to execute accurate CNNs at a reasonably low latency. To maintain data privacy of the public, all processing must remain on the edge-device, without any communication with cloud servers. To address these challenges, we present a low-power binary neural network classifier for correct facial-mask wear and positioning. The classification task is implemented on an embedded FPGA, performing high-throughput binary operations. Classification can take place at up to ~6400 frames-per-second, easily enabling multi-camera, speed-gate settings or statistics collection in crowd settings. When deployed on a single entrance or gate, the idle power consumption is reduced to 1.6W, improving the battery-life of the device. We achieve an accuracy of up to 98% for four wearing positions of the MaskedFace-Net dataset. To maintain equivalent classification accuracy for all face structures, skin-tones, hair types, and mask types, the algorithms are tested for their ability to generalize the relevant features over all subjects using the Grad-CAM approach.
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
Authors: Zhixian Chen, Tengfei Ma, Yangqiu Song, Yang Wang
Date: 2021-02-06 00:00:00
Tasks: Attribute, Graph Neural Network, Graph Representation Learning, Imputation, Matrix Completion, Missing Values, Node Classification, Representation Learning

Missing node attributes is a common problem in real-world graphs. Graph neural networks have been demonstrated power in graph representation learning while their performance is affected by the completeness of graph information. Most of them are not specified for missing-attribute graphs and fail to leverage incomplete attribute information effectively. In this paper, we propose an innovative node representation learning framework, Wasserstein Graph Neural Network (WGNN), to mitigate the problem. To make the most of limited observed attribute information and capture the uncertainty caused by missing values, we express nodes as low-dimensional distributions derived from the decomposition of the attribute matrix. Furthermore, we strengthen the expressiveness of representations by developing a novel message passing schema that aggregates distributional information from neighbors in the Wasserstein space. We test WGNN in node classification tasks under two missing-attribute cases on both synthetic and real-world datasets. In addition, we find WGNN suitable to recover missing values and adapt them to tackle matrix completion problems with graphs of users and items. Experimental results on both tasks demonstrate the superiority of our method.
Federated Reconstruction: Partially Local Federated Learning
Authors: Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash
Date: 2021-02-05 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Collaborative Filtering, Federated Learning, Meta-Learning

Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients to communicate all model parameters can be undesirable due to privacy and communication constraints. Other approaches require always-available or stateful clients, impractical in large-scale cross-device settings. We introduce Federated Reconstruction, the first model-agnostic framework for partially local federated learning suitable for training and inference at scale. We motivate the framework via a connection to model-agnostic meta learning, empirically demonstrate its performance over existing approaches for collaborative filtering and next word prediction, and release an open-source library for evaluating approaches in this setting. We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.
Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets
Authors: Dominik Drees, Aaron Scherzinger, René Hägerling, Friedemann Kiefer, Xiaoyi Jiang
Date: 2021-02-05 00:00:00
Tasks: Foreground Segmentation

Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. We present a scalable pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. Only a single, dimensionless, a-priori determinable parameter is required. By careful engineering of individual pipeline stages and a novel iterative refinement scheme we are, for the first time, able to analyze the topology of volumes of roughly 1TB on commodity hardware. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).
Custom Object Detection via Multi-Camera Self-Supervised Learning
Authors: Yan Lu, Yuanchao Shu
Date: 2021-02-05 00:00:00
Tasks: Object, object-detection, Object Detection, Self-Supervised Learning

This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks. MCSSL associates bounding boxes between cameras with overlapping fields of view by leveraging epipolar geometry and state-of-the-art tracking and reID algorithms, and prudently generates two sets of pseudo-labels to fine-tune backbone and detection networks respectively in an object detection model. To train effectively on pseudo-labels,a powerful reID-like pretext task with consistency loss is constructed for model customization. Our evaluation shows that compared with legacy selftraining methods, MCSSL improves average mAP by 5.44% and 6.76% on WildTrack and CityFlow dataset, respectively.
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes
Authors: Marcus M. Noack, James A. Sethian
Date: 2021-02-05 00:00:00
Tasks: Gaussian Processes, regression, Uncertainty Quantification

Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification. The technique has gained popularity recently in the machine learning community due to its robustness and interpretability. The mathematical methods we discuss in this paper are an extension of the Gaussian-process framework. We are proposing advanced kernel designs that only allow for functions with certain desirable characteristics to be elements of the reproducing kernel Hilbert space (RKHS) that underlies all kernel methods and serves as the sample space for Gaussian process regression. These desirable characteristics reflect the underlying physics; two obvious examples are symmetry and periodicity constraints. In addition, non-stationary kernel designs can be defined in the same framework to yield flexible multi-task Gaussian processes. We will show the impact of advanced kernel designs on Gaussian processes using several synthetic and two scientific data sets. The results show that including domain knowledge, communicated through advanced kernel designs, has a significant impact on the accuracy and relevance of the function approximation.