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A Experimental Study to Invariance of Several Groups Action to the Input of Residual Networks
Yang XY(杨秀义)1,2; Han Z(韩志)1; Tang YD(唐延东)1; Han JD(韩建达)1
Department机器人学研究室
Conference Name2018 IEEE International Conference on Information and Automation, ICIA 2018
Conference DateAugust 11-13, 2018
Conference PlaceWuyishan, China
Source PublicationProceeding of the 2018 IEEE International Conference on Information and Automation
PublisherIEEE
Publication PlaceNew York
2018
Pages1263-1267
Indexed ByEI
EI Accession number20193807458076
Contribution Rank1
ISBN978-1-5386-8069-8
KeywordInvariance Residual network Group action
AbstractWe present a fresh new hypothesis that CNN is insensitive to some variants from input training data example, these variants relate to original training inputs by group actions, and will verify this hypothesis experimentally. This hypothesis means CNNs can generalize well on these variants when training on randomly generated training data and illuminates the paradox Why CNNs fit real and noise data and fail drastically when making predictions for noise data. Our findings suggest the study about generalization theory of CNNs should consider into these invariance of group actions to the input training data samples.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23839
Collection机器人学研究室
Corresponding AuthorHan JD(韩建达)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Yang XY,Han Z,Tang YD,et al. A Experimental Study to Invariance of Several Groups Action to the Input of Residual Networks[C]. New York:IEEE,2018:1263-1267.
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