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Poster
Dynamic Network Surgery for Efficient DNNs
Yiwen Guo · Anbang Yao · Yurong Chen

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #146
in Wednesday Posters »
Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of $\bm{108}\times$ and $\bm{17.7}\times$ respectively, proving that it outperforms the recent pruning method by considerable margins. Code and some models are available at https://github.com/yiwenguo/Dynamic-Network-Surgery.
Keywords: Deep Learning or Neural Networks · (Application) Object and Pattern Recognition · Large Scale Learning and Big Data · Model Selection and Structure Learning
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Author Information

Yiwen Guo (Intel Labs China)
Anbang Yao (Intel Labs China)
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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