CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Self-Paced AutoEncoder
Yu, Tingzhao1,2; Guo, Chaoxu1,2; Wang, Lingfeng1; Xiang, Shiming1; Pan, Chunhong1
Source PublicationIEEE SIGNAL PROCESSING LETTERS
ISSN1070-9908
2018-07-01
Volume25Issue:7Pages:1054-1058
Corresponding AuthorYu, Tingzhao(tingzhao.yu@nlpr.ia.ac.cn)
AbstractAutoencoder, which learns latent representations of samples in an unsupervised manner, has great potential in computer vision and signal processing. However, the diversity of samples makes learning a component autoencoder remaining a challenging task. This letter proposes a novel Self-Paced AutoEncoder (SPAE) for unsupervised feature extraction. The motivation behind this letter is to take samples gradually from simple to complex into consideration during training, which is similar to the mechanism of knowledge acquisition for humans. Under the unsupervised learning framework constructed on the autoencoder infrastructure, our SPAE first learns a weak autoencoder via samples with small losses and, then, elevates itself to a relatively strong autoencoder through samples with large losses. Then, the SPAE is generalized to a temporal domain, resulting to temporal SPAE (TSPAE), where the temporal information is explored and exploited to improve the performance. Typically, a TSPAE is capable of compressing temporal sequences into temporal-independent data. Experiments on the image classification and action recognition demonstrate the effectiveness of SPAE and TSPAE.
KeywordAutoencoder (AE) self-paced learning (SPL) temporal encoding (TE) video analysis
DOI10.1109/LSP.2018.2843295
WOS KeywordREPRESENTATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61773377]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000435520200006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23619
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorYu, Tingzhao
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yu, Tingzhao,Guo, Chaoxu,Wang, Lingfeng,et al. Self-Paced AutoEncoder[J]. IEEE SIGNAL PROCESSING LETTERS,2018,25(7):1054-1058.
APA Yu, Tingzhao,Guo, Chaoxu,Wang, Lingfeng,Xiang, Shiming,&Pan, Chunhong.(2018).Self-Paced AutoEncoder.IEEE SIGNAL PROCESSING LETTERS,25(7),1054-1058.
MLA Yu, Tingzhao,et al."Self-Paced AutoEncoder".IEEE SIGNAL PROCESSING LETTERS 25.7(2018):1054-1058.
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