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计算机系统应用:2020,29(1):14-21
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基于深度残差网络图像分类算法研究综述
(1.中国科学院 计算机网络信息中心, 北京 100190;2.中国科学院大学, 北京 100049;3.四川卧龙国家级自然保护区管理局, 卧龙 623006)
Survey on Image Classification Algorithms Based on Deep Residual Network
(1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Sichuan Wolong National Natural Reseve Administration, Wolong 623006, China)
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投稿时间:2019-06-20    修订日期:2019-07-16
中文摘要: 近年来,由于计算机技术的飞速迅猛发展,特别是硬件条件的改善,计算能力不断提高,深层神经网络训练的时间大大缩短,深度残差网络也迅速成为一个新的研究热点.深度残差网络作为一种极深的网络架构,在精度和收敛等方面都展现出了很好的特性.研究者们深入研究其本质并在此基础上提出了很多关于深度残差网络的改进,如宽残差网络,金字塔型残差网络,密集型残差网络,注意力残差网络等等.本文从残差网络的设计出发,分析了不同残差单元的构造方式,介绍了深度残差网络不同的变体.从不同的角度比较了不同网络之间的差异以及这些网络架构在常用图像分类数据集上的性能表现.最后我们对于这些网络进行了总结,并讨论了未来深度残差网络在图像分类领域的一些研究方向.
Abstract:Recently, the training time of deep neural network has been greatly shortened because of the rapid development of computer technology especially the improvement of hardware conditions. The deep residual network has rapidly become a new research hotspot. The architecture exhibits good features in terms of precision and convergence. Researchers have delved into its nature and proposed many improvements on deep residual networks, such as wide residual networks, deep pyramidal residual networks, densely residual networks, attention residual network, etc. This study analyzes the construction of different residual units from the design of residual network, and introduces different variants of deep residual network. From different aspects, We compare the differences between different networks and the performance of these network architectures on common image classification datasets. Finally, we summarize these networks and discuss some research directions of future deep residual networks in the field of image classification.
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基金项目:中国科学院科技服务网络计划区域重点项目(Y82E01,KFJ-STS-QYZD-058);国家研发基础设施和设施发展计划(DKA2018-12-02-XX);中国科学院战略性先导科技专项(XDA19060205);中科院信息化专项(XXH13505-03-205,XXH13506-305,XXH13506-303);中国科学院计算机网络信息中心一三五规划重点培育方向(CNIC-PY-1408,CNIC_PY_1409);专项大熊猫国际合作资金
引用文本:
赵志成,罗泽,王鹏彦,李健.基于深度残差网络图像分类算法研究综述.计算机系统应用,2020,29(1):14-21
ZHAO Zhi-Cheng,LUO Ze,WANG Peng-Yan,LI Jian.Survey on Image Classification Algorithms Based on Deep Residual Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):14-21

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