CASIA OpenIR  > 图像与视频分析团队
From Hashing to CNNs: Training BinaryWeight Networks via Hashing
Hu, Qinghao1,2; Wang, Peisong1,2; Cheng, Jian1,2,3
2018-02
Conference NameThe Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)
Conference Date2018年2月2-8日
Conference Place美国新奥尔良
Abstract

Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to real-world applications. However, most of state-of-art CNNs require large memory and computational resources, which hinders the deployment on mobile devices. Recent studies show that low-bit weight representation can reduce much storage and memory demand, and also can achieve efficient network inference. To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing. In this paper, we first reveal the strong connection between inner-product preserving hashing and binary weight networks, and show that training binary weight networks can be intrinsically regarded as a hashing problem. Based on this perspective, we propose an alternating optimization method to learn the hash codes instead of directly learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by a large margin. 

KeywordHashing Binary Weight Network Cnns
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23703
Collection图像与视频分析团队
Corresponding AuthorCheng, Jian
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hu, Qinghao,Wang, Peisong,Cheng, Jian. From Hashing to CNNs: Training BinaryWeight Networks via Hashing[C],2018.
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