A Light CNN for Deep Face Representation With Noisy Labels

doi:10.1109/TIFS.2018.2833032

CASIA OpenIR > 模式识别实验室

	A Light CNN for Deep Face Representation With Noisy Labels
	Wu, Xiang1,2,3,4 ; He, Ran1,2,3,4 ; Sun, Zhenan1,2,3,4 ; Tan, Tieniu1,2,3,4
发表期刊	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN	1556-6013
	2018-11-01
卷号	13 期号:11 页码:2884-2896
通讯作者	Sun, Zhenan(znsun@nlpr.ia.ac.cn)
摘要	The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When training data are obtained from the Internet, the labels are likely to be ambiguous and inaccurate. This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. First, we introduce a variation of maxout activation, called max-feature-map (MFM), into each convolutional layer of CNN. Different from maxout activation that uses many feature maps to linearly approximate an arbitrary convex activation function, MFM does so via a competitive relationship. MFM can not only separate noisy and informative signals but also play the role of feature selection between two feature maps. Second, three networks are carefully designed to obtain better performance, meanwhile, reducing the number of parameters and computational costs. Finally, a semantic bootstrapping method is proposed to make the prediction of the networks more consistent with noisy labels. Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces. The learned single network with a 256-D representation achieves state-of-theart results on various face benchmarks without fine-tuning.
关键词	Convolutional neural network face recognition
DOI	10.1109/TIFS.2018.2833032
关键词[WOS]	RECOGNITION ; CLASSIFICATION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61622310] ; State Key Development Program[2016YFB1001001] ; State Key Development Program[2016YFB1001001] ; National Natural Science Foundation of China[61622310] ; National Natural Science Foundation of China[61427811]
项目资助者	State Key Development Program ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000433909100013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：645[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21077
专题	模式识别实验室
通讯作者	Sun, Zhenan
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China 2.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing 100864, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100864, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wu, Xiang,He, Ran,Sun, Zhenan,et al. A Light CNN for Deep Face Representation With Noisy Labels[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2018,13(11):2884-2896.
APA	Wu, Xiang,He, Ran,Sun, Zhenan,&Tan, Tieniu.(2018).A Light CNN for Deep Face Representation With Noisy Labels.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,13(11),2884-2896.
MLA	Wu, Xiang,et al."A Light CNN for Deep Face Representation With Noisy Labels".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13.11(2018):2884-2896.