Variational Automatic Channel Pruning Algorithm Based on Structure Optimization for Convolutional Neural Networks,ERICDATA高等教育知識庫
高等教育出版
熱門: 謝傳崇  羅文君  葉俊廷  紀金山  簡淑芸  SDGs  
高等教育出版
首頁 臺灣期刊   學校系所   學協會   民間出版   大陸/海外期刊   政府機關   學校系所   學協會   民間出版   DOI註冊服務
閱讀全文
篇名
Variational Automatic Channel Pruning Algorithm Based on Structure Optimization for Convolutional Neural Networks
並列篇名
Variational Automatic Channel Pruning Algorithm Based on Structure Optimization for Convolutional Neural Networks
作者 Shuo HanYufei ZhanXingang Liu
英文摘要
Channel pruning has achieved remarkable success in solving the significant computation and memory consumption in Convolutional Neural Networks (CNN). Most existing methods measure the importance of channels with manually designed algorithms and pruning unimportant channels rely on heuristics or expertise during the processing, which are labourious and subjective. In this paper, we proposed a Variational Automatic Channel Pruning Algorithm based on structure optimization (VA-CPSO) which can automatically optimize channel numbers via channel scales in end-toend manner through variational inference. Firstly, a weights generator controlled by channel scales is built to produce weights for various pruned structure of CNN. And then, the channel scales with truncated factorized log-uniform prior and log-normal posterior are optimized by variational inference for optimal pruning structure. Meanwhile, parameters of the weights generator are optimized synchronously. Finally, the acquired optimal structure and corresponding generated weights are deployed in the pruned CNN for further training to achieve high-performance compression. The experimental results demonstrate that our proposed VACPSO acquires better compression performance compared to existing pruning algorithms. The VA-CPSO achieve a compression of 34.60×, 4.28×, 1.96× and a speedup of 28.20×, 2.03×, 2.02× on LeNet-5, VGGNet, and ResNet-110 with no more than 0.5% loss of accuracy.
起訖頁 339-351
關鍵詞 Automatic channel pruningVariational inferenceStructure optimizationCNN compressionTruncated distributions
刊名 網際網路技術學刊  
期數 202103 (22:2期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642021032202009  複製DOI
QR Code
該期刊
上一篇
Blockchain-enabled Charging Scheduling for Unmanned Vehicles in Smart Cities
該期刊
下一篇
Energy Conserving Forepart Detection Scheme with Dynamic Compressive Measurements Based on Compressive Sensing for WVSN

高等教育知識庫  閱讀計畫  教育研究月刊  新書優惠  

教師服務
合作出版
期刊徵稿
聯絡高教
高教FB
讀者服務
圖書目錄
教育期刊
訂購服務
活動訊息
數位服務
高等教育知識庫
國際資料庫收錄
投審稿系統
DOI註冊
線上購買
高點網路書店 
元照網路書店
博客來網路書店
教育資源
教育網站
國際教育網站
關於高教
高教簡介
出版授權
合作單位
知識達 知識達 知識達 知識達 知識達 知識達
版權所有‧轉載必究 Copyright2011 高等教育文化事業有限公司  All Rights Reserved
服務信箱:edubook@edubook.com.tw 台北市館前路 26 號 6 樓 Tel:+886-2-23885899 Fax:+886-2-23892500