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卷积神经网络结构优化综述

林景栋 吴欣怡 柴毅 尹宏鹏

林景栋, 吴欣怡, 柴毅, 尹宏鹏. 卷积神经网络结构优化综述. 自动化学报, 2020, 46(1): 24-37. doi: 10.16383/j.aas.c180275
引用本文: 林景栋, 吴欣怡, 柴毅, 尹宏鹏. 卷积神经网络结构优化综述. 自动化学报, 2020, 46(1): 24-37. doi: 10.16383/j.aas.c180275
LIN Jing-Dong, WU Xin-Yi, CHAI Yi, YIN Hong-Peng. Structure Optimization of Convolutional Neural Networks: A Survey. ACTA AUTOMATICA SINICA, 2020, 46(1): 24-37. doi: 10.16383/j.aas.c180275
Citation: LIN Jing-Dong, WU Xin-Yi, CHAI Yi, YIN Hong-Peng. Structure Optimization of Convolutional Neural Networks: A Survey. ACTA AUTOMATICA SINICA, 2020, 46(1): 24-37. doi: 10.16383/j.aas.c180275

卷积神经网络结构优化综述

doi: 10.16383/j.aas.c180275
基金项目: 

国家自然科学基金 61633005

国家自然科学基金 61773080

中央高校基本科研业务费专项资金 2019CDYGZD001

重庆市基础科学与研究技术专项 cstc2015jcyjB0569

重庆大学科研后备拔尖人才 cqu2018CDHB1B04

重庆市重点科技专项子项 cstc2015shms-ztzx30001

详细信息
    作者简介:

    林景栋  重庆大学自动化学院副教授. 2002年获得重庆大学博士学位.主要研究方向为工业自动化生产线设计, 智能家居控制系统的设计.E-mail: linzhanding@163.com

    吴欣怡  重庆大学自动化学院硕士研究生. 2016年获得重庆大学学士学位.主要研究方向为深度学习, 计算机视觉. E-mail: wuxinyi12358@gmail.com

    柴毅  重庆大学自动化学院教授.2001年获得重庆大学博士学位.主要研究方向为信息处理, 融合与控制, 计算机网络与系统控制. E-mail:chaiyi@cqu.edu.cn

    通讯作者:

    尹宏鹏  重庆大学自动化学院教授.2009年获得重庆大学博士学位.主要研究方向为模式识别与智能系统.本文通信作者.E-mail:yinhongpeng@gmail.com

Structure Optimization of Convolutional Neural Networks: A Survey

Funds: 

National Natural Science Foundation of China 61633005

National Natural Science Foundation of China 61773080

Fundamental Research Funds for the Central Universities 2019CDYGZD001

Chongqing Nature Science Foundation of Fundamental Science and Frontier Technologies cstc2015jcyjB0569

Scientiflc Reserved Talents of Chongqing University cqu2018CDHB1B04

Chongqing Nature Science Foundation of Scientiflc Key Program cstc2015shms-ztzx30001

More Information
    Author Bio:

    LIN Jing-Dong   Associate professor at the College of Automation, Chongqing University. He received his Ph. D. degree from Chongqing University in 2002. His research interest covers industrial automation line design, and smart home control system design

    WU Xin-Yi   Master student at the College of Automation, Chongqing University. He received his bachelor degree from Chongqing University in 2016. His research interest covers deep learning and computer vision

    CHAI Yi   Professor at the College of Automation, Chongqing University. He received his Ph. D. degree from Chongqing University in 2001. His research interest covers information processing, integration and control, and computer network and system control

    Corresponding author: YIN Hong-Peng   Professor at the College of Automation, Chongqing University. He received his Ph. D. degree from Chongqing University in 2009. His research interest covers pattern recognition, image processing, and computer vision. Corresponding author of this paper
  • 摘要: 近年来, 卷积神经网络(Convolutional neural network, CNNs)在计算机视觉、自然语言处理、语音识别等领域取得了突飞猛进的发展, 其强大的特征学习能力引起了国内外专家学者广泛关注.然而, 由于深度卷积神经网络普遍规模庞大、计算度复杂, 限制了其在实时要求高和资源受限环境下的应用.对卷积神经网络的结构进行优化以压缩并加速现有网络有助于深度学习在更大范围的推广应用, 目前已成为深度学习社区的一个研究热点.本文整理了卷积神经网络结构优化技术的发展历史、研究现状以及典型方法, 将这些工作归纳为网络剪枝与稀疏化、张量分解、知识迁移和精细模块设计4个方面并进行了较为全面的探讨.最后, 本文对当前研究的热点与难点作了分析和总结, 并对网络结构优化领域未来的发展方向和应用前景进行了展望.
    Recommended by Associate Editor HE Wei
    1)  本文责任编委 贺威
  • 图  1  LeNet-5网络结构[6]

    Fig.  1  Structure of LeNet-5[6]

    图  2  四种剪枝粒度方式[26]

    Fig.  2  Four pruning granularities[26]

    图  3  张量分解过程

    Fig.  3  Process of tensor factorization

    图  4  知识迁移过程

    Fig.  4  Process of knowledge transfer

    图  5  Inception-v1结构[4]

    Fig.  5  Inception-v1 module[4]

    图  6  卷积核分解示意图[57]

    Fig.  6  Process of convolutional filter factorization[57]

    图  7  Xception模块[59]

    Fig.  7  Xception module[59]

    图  8  线性卷积结构与多层感知机卷积结构[55]

    Fig.  8  Linear convolutional structure and Mlpconv structure[55]

    图  9  残差模块[5]

    Fig.  9  Residual module[5]

    表  1  经典卷积神经网络的性能及相关参数

    Table  1  Classic convolutional neural networks and corresponding parameters

    年份 网络名称 网络层数 卷积层数量 参数数量 卷积层 全连接层 乘加操作数(MACs) 卷积层 全连接层 Top-5错误率(%)
    2012 AlexNet[1] 8 5 2.3M 58.6M 666 M 58.6M 16.4
    2014 Overfeat[2] 8 5 16 M 130M 2.67G 124M 14.2
    2014 VGGNet-16[3] 16 13 14.7M 124M 15.3 G 130M 7.4
    2015 GoogLeNet[4] 22 21 6M 1M 1.43 G 1M 6.7
    2016 ResNet-50[5] 50 49 23.5M 2M 3.86 G 2M 3.6
    下载: 导出CSV

    表  2  网络剪枝对不同网络的压缩效果

    Table  2  Comparison of different pruned networks

    所用方法 选用网络 初始错误率 剪枝后错误率 初始参数量 剪枝后参数量 压缩率
    [28] AlexNet 19.73% 19.70% 61 M 6.7M
    [29] CaffeNet 42.16% 44.4% 61 M 21.3M
    [30] LeNet-5 0.91% 0.91 % 431 K 4.0 K 108×
    [33] VGGNet-16 6.75% 6.6% 150 M 5.4M 28×
    [34] ResNet-50 8.86% 11.7% 25.56 M 8.66M
    下载: 导出CSV
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  • 录用日期:  2018-11-05
  • 刊出日期:  2020-01-21

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    返回文章
    返回