Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks
文献类型:期刊论文
作者 | Chen, Zhiqiang1,3![]() ![]() ![]() ![]() ![]() |
刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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出版日期 | 2020-04 |
卷号 | 无期号:无页码:无 |
关键词 | Conditional accuracy change (CAC), direct criterion, dynamical channel pruning, neural network compression, structure shaping. |
ISSN号 | 1045-9227 |
DOI | 10.1109/TNNLS.2020.2979517 |
通讯作者 | He, Huiguang(huiguang.he@ia.ac.cn) |
文献子类 | 长文 |
英文摘要 | Channel pruning is an effective technique that has been widely applied to deep neural network compression. However, many existing methods prune from a pretrained model, thus resulting in repetitious pruning and fine-tuning processes. In this article, we propose a dynamical channel pruning method, which prunes unimportant channels at the early stage of training. Rather than utilizing some indirect criteria (e.g., weight norm, absolute weight sum, and reconstruction error) to guide connection or channel pruning, we design criteria directly related to the final accuracy of a network to evaluate the importance of each channel. Specifically, a channelwise gate is designed to randomly enable or disable each channel so that the conditional accuracy changes (CACs) can be estimated under the condition of each channel disabled. Practically, we construct two effective and efficient criteria to dynamically estimate CAC at each iteration of training; thus, unimportant channels can be gradually pruned during the training process. Finally, extensive experiments on multiple data sets (i.e., ImageNet, CIFAR, and MNIST) with various networks (i.e., ResNet, VGG, and MLP) demonstrate that the proposed method effectively reduces the parameters and computations of baseline network while yielding the higher or competitive accuracy. Interestingly, if we Double the initial Channels and then Prune Half (DCPH) of them to baseline’s counterpart, it can enjoy a remarkable performance improvement by shaping a more desirable structure |
URL标识 | 查看原文 |
资助项目 | National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[61721004] ; CAS International Collaboration Key Project ; Strategic Priority Research Program of CAS[XDB32040200] |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
WOS记录号 | WOS:000616310400027 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
资助机构 | National Natural Science Foundation of China ; CAS International Collaboration Key Project ; Strategic Priority Research Program of CAS |
源URL | [http://ir.ia.ac.cn/handle/173211/42215] ![]() |
专题 | 类脑智能研究中心_神经计算及脑机交互 |
通讯作者 | He, Huiguang |
作者单位 | 1.University of Chinese Academy of Sciences (UCAS), Beijing 100049, China 2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China 3.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China 4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China |
推荐引用方式 GB/T 7714 | Chen, Zhiqiang,Xu, Ting-Bing,Du, Changde,et al. Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,无(无):无. |
APA | Chen, Zhiqiang,Xu, Ting-Bing,Du, Changde,Liu, Cheng-Lin,&He, Huiguang.(2020).Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,无(无),无. |
MLA | Chen, Zhiqiang,et al."Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 无.无(2020):无. |
入库方式: OAI收割
来源:自动化研究所
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