Improving Convolutional Neural Networks with Competitive Activation Function

doi:10.1155/2021/1933490

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	Improving Convolutional Neural Networks with Competitive Activation Function
	Ying, Yao 1; Zhang, Nengbo 2; He, Ping 3,4; Peng, Silong 5
刊名	SECURITY AND COMMUNICATION NETWORKS
	2021-05-13
卷号	2021 页码:9
ISSN号	1939-0114
DOI	10.1155/2021/1933490
通讯作者	He, Ping(pinghecn@qq.com)
英文摘要	The activation function is the basic component of the convolutional neural network (CNN), which provides the nonlinear transformation capability required by the network. Many activation functions make the original input compete with different linear or nonlinear mapping terms to obtain different nonlinear transformation capabilities. Until recently, the original input of funnel activation (FReLU) competed with the spatial conditions, so FReLU not only has the ability of nonlinear transformation but also has the ability of pixelwise modeling. We summarize the competition mechanism in the activation function and then propose a novel activation function design template: competitive activation function (CAF), which promotes competition among different elements. CAF generalizes all activation functions that use competition mechanisms. According to CAF, we propose a parametric funnel rectified exponential unit (PFREU). PFREU promotes competition among linear mapping, nonlinear mapping, and spatial conditions. We conduct experiments on four datasets of different sizes, and the experimental results of three classical convolutional neural networks proved the superiority of our method.
资助项目	National Natural Science Foundation of China[11705122] ; Science and Technology Program of Sichuan[2020YFH0124] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515011342] ; Zigong Key Science and Technology Project of China[2020YGJC01]
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	WILEY-HINDAWI
WOS记录号	WOS:000668944400001
资助机构	National Natural Science Foundation of China ; Science and Technology Program of Sichuan ; Guangdong Basic and Applied Basic Research Foundation ; Zigong Key Science and Technology Project of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45255]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	He, Ping
作者单位	1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China 2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 3.Jinan Univ, Sch Intelligent Syst Sci & Engn, Guangzhou 519070, Guangdong, Peoples R China 4.Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab Sichuan Prov, Zigong 643000, Sichuan, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ying, Yao,Zhang, Nengbo,He, Ping,et al. Improving Convolutional Neural Networks with Competitive Activation Function[J]. SECURITY AND COMMUNICATION NETWORKS,2021,2021:9.
APA	Ying, Yao,Zhang, Nengbo,He, Ping,&Peng, Silong.(2021).Improving Convolutional Neural Networks with Competitive Activation Function.SECURITY AND COMMUNICATION NETWORKS,2021,9.
MLA	Ying, Yao,et al."Improving Convolutional Neural Networks with Competitive Activation Function".SECURITY AND COMMUNICATION NETWORKS 2021(2021):9.