pwc-archive/papers-with-abstracts huggingface dataset:
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
Authors: Yunji Kim, Jung-Woo Ha
Date: 2021-12-30 00:00:00
Proceeding: ICLR 2022 4
Tasks: Clustering, Contrastive Learning

Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN with contrastive learning. We aim to learn feature representations that encourage a dataset to form distinct cluster boundaries in the embedding space, while also maximizing the mutual information between the latent code and its image observation. Our approach is to train a discriminator, which is also used for inferring clusters, to optimize the contrastive loss, where image-latent pairs that maximize the mutual information are considered as positive pairs and the rest as negative pairs. Specifically, we map the input of a generator, which was sampled from the categorical distribution, to the embedding space of the discriminator and let them act as a cluster centroid. In this way, C3-GAN succeeded in learning a clustering-friendly embedding space where each cluster is distinctively separable. Experimental results show that C3-GAN achieved the state-of-the-art clustering performance on four fine-grained image datasets, while also alleviating the mode collapse phenomenon. Code is available at https://github.com/naver-ai/c3-gan.
A Novel Generator with Auxiliary Branch for Improving GAN Performance
Authors: Seung Park, Yong-Goo Shin
Date: 2021-12-30 00:00:00
Tasks: Generative Adversarial Network, Image Generation

The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and the latter ones refine the details. To propagate the coarse information well, recent works usually build their generators by stacking up multiple residual blocks. Although the residual block can produce a high-quality image as well as be trained stably, it often impedes the information flow in the network. To alleviate this problem, this brief introduces a novel generator architecture that produces the image by combining features obtained through two different branches: the main and auxiliary branches. The goal of the main branch is to produce the image by passing through the multiple residual blocks, whereas the auxiliary branch is to convey the coarse information in the earlier layer to the later one. To combine the features in the main and auxiliary branches successfully, we also propose a gated feature fusion module that controls the information flow in those branches. To prove the superiority of the proposed method, this brief provides extensive experiments using various standard datasets including CIFAR-10, CIFAR-100, LSUN, CelebA-HQ, AFHQ, and tiny-ImageNet. Furthermore, we conducted various ablation studies to demonstrate the generalization ability of the proposed method. Quantitative evaluations prove that the proposed method exhibits impressive GAN performance in terms of Inception score (IS) and Frechet inception distance (FID). For instance, the proposed method boosts the FID and IS scores on the tiny-ImageNet dataset from 35.13 to 25.00 and 20.23 to 25.57, respectively.
Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System
Authors: Runze Gao, Yuanqing Xia, Guan Wang, Liwen Yang, Yufeng Zhan
Date: 2021-12-29 00:00:00

Subspace identification (SID) has been widely used in system identification and control fields since it can estimate system models only relying on the input and output data by reliable numerical operations such as singular value decomposition (SVD). However, high-dimension Hankel matrices are involved to store these data and used to obtain the system models, which increases the computation amount of SID and leads SID not suitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing and container technology is proposed to accelerate the traditional algorithm. First, a workflow-based structure of SID is designed to match the distributed cloud environment, based on the computational feature of each calculation stage. Second, a containerised cloud workflow processing system is established to execute the logic- and data- dependent SID workflow mission based on Kubernetes system. Finally, the experiments show that the computation time is reduced by at most $91.6\%$ for large-scale SID mission and decreased to within 20 ms for the real-time mission parameter.
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences
Authors: Takehiro Tanaka, Hyomin Choi, Ivan V. Bajić
Date: 2021-12-30 00:00:00
Tasks: Object, Object Tracking, Video Compression

We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. Ground-truth annotations for 13 sequences were prepared and released as the dataset called SFU-HW-Tracks-v1. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking.
K-Core Decomposition on Super Large Graphs with Limited Resources
Authors: Shicheng Gao, Jie Xu, Xiaosen Li, Fangcheng Fu, Wentao Zhang, Wen Ouyang, Yangyu Tao, Bin Cui
Date: 2021-12-26 00:00:00

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition
Authors: Jianghao Shen, Tianfu Wu
Date: 2021-12-29 00:00:00
Tasks: Image Classification, Image Generation, One-Shot Learning, Specificity

Learning light-weight yet expressive deep networks in both image synthesis and image recognition remains a challenging problem. Inspired by a more recent observation that it is the data-specificity that makes the multi-head self-attention (MHSA) in the Transformer model so powerful, this paper proposes to extend the widely adopted light-weight Squeeze-Excitation (SE) module to be spatially-adaptive to reinforce its data specificity, as a convolutional alternative of the MHSA, while retaining the efficiency of SE and the inductive basis of convolution. It presents two designs of spatially-adaptive squeeze-excitation (SASE) modules for image synthesis and image recognition respectively. For image synthesis tasks, the proposed SASE is tested in both low-shot and one-shot learning tasks. It shows better performance than prior arts. For image recognition tasks, the proposed SASE is used as a drop-in replacement for convolution layers in ResNets and achieves much better accuracy than the vanilla ResNets, and slightly better than the MHSA counterparts such as the Swin-Transformer and Pyramid-Transformer in the ImageNet-1000 dataset, with significantly smaller models.
Deep Learning meets Liveness Detection: Recent Advancements and Challenges
Authors: Arian Sabaghi, Marzieh Oghbaie, Kooshan Hashemifard, Mohammad Akbari
Date: 2021-12-29 00:00:00
Tasks: Deep Learning, Face Anti-Spoofing, Face Presentation Attack Detection

Facial biometrics has been recently received tremendous attention as a convenient replacement for traditional authentication systems. Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing~(FAS),i.e., face presentation attack detection. Deep feature learning and techniques, as opposed to hand-crafted features, have promised a dramatic increase in the FAS systems' accuracy, tackling the key challenges of materializing the real-world application of such systems. Hence, a new research area dealing with the development of more generalized as well as accurate models is increasingly attracting the attention of the research community and industry. In this paper, we present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017. To shed light on this topic, a semantic taxonomy based on various features and learning methodologies is represented. Further, we cover predominant public datasets for FAS in chronological order, their evolutional progress, and the evaluation criteria (both intra-dataset and inter-dataset). Finally, we discuss the open research challenges and future directions.
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint
Authors: Andrea Fresa, Jaya Prakash Champati
Date: 2021-12-21 00:00:00
Tasks: Edge-computing, image-classification, Image Classification

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference, we study the problem of offloading inference jobs by considering the following novel aspects: 1) in contrast to a typical computational job, the processing time of an inference job depends on the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs) for resource-constrained devices provide the choice of scaling the model size. We formulate an assignment problem with the aim of maximizing the total inference accuracy of n data samples available at the ED, subject to a time constraint T on the makespan. We propose an approximation algorithm AMR2, and prove that it results in a makespan at most 2T, and achieves a total accuracy that is lower by a small constant from optimal total accuracy. As proof of concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNet, and is connected to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR2 for image classification application.
Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding
Authors: Erez Yosef, Shay Elmalem, Raja Giryes
Date: 2021-12-28 00:00:00
Tasks: Video Frame Interpolation, Video Reconstruction

Video reconstruction from a single motion-blurred image is a challenging problem, which can enhance the capabilities of existing cameras. Recently, several works addressed this task using conventional imaging and deep learning. Yet, such purely-digital methods are inherently limited, due to direction ambiguity and noise sensitivity. Some works proposed to address these limitations using non-conventional image sensors, however, such sensors are extremely rare and expensive. To circumvent these limitations with simpler means, we propose a hybrid optical-digital method for video reconstruction that requires only simple modifications to existing optical systems. We use a learned dynamic phase-coding in the lens aperture during the image acquisition to encode the motion trajectories, which serve as prior information for the video reconstruction process. The proposed computational camera generates a sharp frame burst of the scene at various frame rates from a single coded motion-blurred image, using an image-to-video convolutional neural network. We present advantages and improved performance compared to existing methods, using both simulations and a real-world camera prototype. We extend our optical coding also to video frame interpolation and present robust and improved results for noisy videos.
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
Authors: Md. Khaledur Rahman, Ariful Azad
Date: 2021-12-20 00:00:00
Tasks: Clustering, CPU, Feature Engineering, GPU, Graph Embedding, Graph Learning, Graph Representation Learning, Link Prediction, Node Classification, Prediction, Representation Learning

Graph representation learning is a fast-growing field where one of the main objectives is to generate meaningful representations of graphs in lower-dimensional spaces. The learned embeddings have been successfully applied to perform various prediction tasks, such as link prediction, node classification, clustering, and visualization. The collective effort of the graph learning community has delivered hundreds of methods, but no single method excels under all evaluation metrics such as prediction accuracy, running time, scalability, etc. This survey aims to evaluate all major classes of graph embedding methods by considering algorithmic variations, parameter selections, scalability, hardware and software platforms, downstream ML tasks, and diverse datasets. We organized graph embedding techniques using a taxonomy that includes methods from manual feature engineering, matrix factorization, shallow neural networks, and deep graph convolutional networks. We evaluated these classes of algorithms for node classification, link prediction, clustering, and visualization tasks using widely used benchmark graphs. We designed our experiments on top of PyTorch Geometric and DGL libraries and run experiments on different multicore CPU and GPU platforms. We rigorously scrutinize the performance of embedding methods under various performance metrics and summarize the results. Thus, this paper may serve as a comparative guide to help users select methods that are most suitable for their tasks.
Efficient Strong Scaling Through Burst Parallel Training
Authors: Seo Jin Park, Joshua Fried, Sunghyun Kim, Mohammad Alizadeh, Adam Belay
Date: 2021-12-19 00:00:00
Tasks: GPU

As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to train DNNs is becoming an essential requirement to achieving acceptable training times. In this paper, we consider the case where future increases in cluster size will cause the global batch size that can be used to train models to reach a fundamental limit: beyond a certain point, larger global batch sizes cause sample efficiency to degrade, increasing overall time to accuracy. As a result, to achieve further improvements in training performance, we must instead consider "strong scaling" strategies that hold the global batch size constant and allocate smaller batches to each GPU. Unfortunately, this makes it significantly more difficult to use cluster resources efficiently. We present DeepPool, a system that addresses this efficiency challenge through two key ideas. First, burst parallelism allocates large numbers of GPUs to foreground jobs in bursts to exploit the unevenness in parallelism across layers. Second, GPU multiplexing prioritizes throughput for foreground training jobs, while packing in background training jobs to reclaim underutilized GPU resources, thereby improving cluster-wide utilization. Together, these two ideas enable DeepPool to deliver a 1.2 - 2.3x improvement in total cluster throughput over standard data parallelism with a single task when the cluster scale is large.
Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications
Authors: Davood G. Samani, Mohsen Amini Salehi
Date: 2021-12-17 00:00:00
Tasks: CPU, GPU, Open-Ended Question Answering

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DL applications -- practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.
Non-Reference Quality Monitoring of Digital Images using Gradient Statistics and Feedforward Neural Networks
Authors: Nisar Ahmed, Hafiz Muhammad Shahzad Asif, Hassan Khalid
Date: 2021-12-27 00:00:00
Tasks: Image Quality Assessment

Digital images contain a lot of redundancies, therefore, compressions are applied to reduce the image size without the loss of reasonable image quality. The same become more prominent in the case of videos that contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of the quality of images in such scenarios becomes of particular interest. Subjective evaluation in most of the scenarios becomes infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the quality score which is not feasible in scenarios such as broadcasting or IP video. Therefore, a non-reference quality metric is proposed to assess the quality of digital images which calculates luminance and multiscale gradient statistics along with mean subtracted contrast normalized products as features to train a Feedforward Neural Network with Scaled Conjugate Gradient. The trained network has provided good regression and R2 measures and further testing on LIVE Image Quality Assessment database release-2 has shown promising results. Pearson, Kendall, and Spearman's correlation are calculated between predicted and actual quality scores and their results are comparable to the state-of-the-art systems. Moreover, the proposed metric is computationally faster than its counterparts and can be used for the quality assessment of image sequences.
Reproducible and Portable Big Data Analytics in the Cloud
Authors: Xin Wang, Pei Guo, Xingyan Li, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman, Jianwu Wang
Date: 2021-12-17 00:00:00
Tasks: Cloud Computing, CPU, Descriptive, GPU

Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first is how to automate end-to-end scalable execution of analytics including distributed environment provisioning, analytics pipeline description, parallel execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics.
MedShift: identifying shift data for medical dataset curation
Authors: Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, Imon Banerjee
Date: 2021-12-27 00:00:00

To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or variance in data have not been significantly researched. Challenges to this are the lack of effective approaches to learn dense representation of a dataset and difficulties of sharing private data across medical institutions. To overcome the problems, we propose a unified pipeline called MedShift to detect the top-level shift samples and thus facilitate the medical curation. Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way. Second, without exchanging data across sources, we run the trained anomaly detectors on an external dataset B for each class. The data samples with high anomaly scores are identified as shift data. To quantify the shiftness of the external dataset, we cluster B's data into groups class-wise based on the obtained scores. We then train a multi-class classifier on A and measure the shiftness with the classifier's performance variance on B by gradually dropping the group with the largest anomaly score for each class. Additionally, we adapt a dataset quality metric to help inspect the distribution differences for multiple medical sources. We verify the efficacy of MedShift with musculoskeletal radiographs (MURA) and chest X-rays datasets from more than one external source. Experiments show our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently. An interface introduction video to visualize our results is available at https://youtu.be/V3BF0P1sxQE.