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Poster
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo · Chao Zhang · Changshui Zhang · Yurong Chen

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 210 #21
in Thu Poster Session B »
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under $l_2$ attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.
Keywords: Sparsity and Compressed Sensing · Adversarial Networks · Efficient Inference Methods · CNN Architectures
[ Paper] 

Author Information

Yiwen Guo (Intel Labs China)
Chao Zhang (Peking University)
Changshui Zhang (Tsinghua University)
Yurong Chen (Intel Labs China)

Dr. Yurong Chen is a Principal Research Scientist and Sr. Research Director at Intel Corporation, and Director of Cognitive Computing Lab at Intel Labs China. Currently, he’s responsible for leading cutting-edge Visual Cognition and Machine Learning research for Intel smart computing and driving research innovation in smart visual data processing technologies on Intel platforms across Intel Labs. He drove the research and development of Deep Learning (DL) based Visual Understanding (VU) and leading Face Analysis technologies to impact Intel architectures/platforms and delivered core technologies to help differentiate Intel products including Intel Movidius VPU, RealSense SDK, CV SDK, OpenVINO, Unite, IOT video E2E analytics solutions and client apps. His team also delivered core AI technologies such as 3D face technology and tiger Re-ID for “Chris Lee World’s First AI Music Video”, “The Great Wall Restoration” and “Saving Amur Tigers” to promote Intel AI leadership. Meanwhile, his team won and achieved top rankings in many international visual challenges' tasks including image matching and multi-view reconstruction (CVPR 2019), adversarial vision (NeurIPS 2018/17), multimodal emotion recognition (ACM ICMI EmotiW 2017/16/15), object detection (MS COCO 2017), visual question answering (VQA 2017), video description (MSR-VTT 2016), etc. He led the team to win Intel China Award (Top team award of Intel China) 2016, Intel Labs Academic Awards (Top award of Intel labs) – Gordy Award 2016, 2015 and 2014 for outstanding research achievements on DL based VU, Multimodal Emotion Recognition and Advanced Visual Analytics. He has published over 60 technical papers (in CVPR, ICCV, ECCV, TPAMI, IJCV, NeurIPS, ICLR, IJCAI, IEEE Micro, etc.) and holds 50+ issued/pending US/PCT patents. Dr. Chen joined Intel in 2004 after finishing his postdoctoral research in the Institute of Software, CAS. He received his Ph.D. degree from Tsinghua University in 2002.

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