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Paper Abstract and Keywords
Presentation 2022-02-21 12:45
Regularizing Generative Adversarial Networks with Internal Representation of Generators
Yusuke Hara, Toshihiko Yamasaki (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) In training generative adversarial networks, maintaining the criteria of the discriminator stably is crucial to training speed and quality. To address this issue, we propose a new regularization term which makes the discriminator learn to estimate information deriving from the internal representation of the generator. Moreover, we propose a new consistency regularization (CR) on the estimation. While existing CR methods using the real-or-fake output allow the generator to learn the augmented distribution, our CR suppresses this problem.
Keyword (in Japanese) (See Japanese page) 
(in English) Generative Adversarial Networks / Image Generation / Deep Learning / / / / /  
Reference Info. ITE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IEICE-IE 
Conference Code 2022-02-IE-ITS-AIT-ME-MMS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Regularizing Generative Adversarial Networks with Internal Representation of Generators 
Sub Title (in English)  
Keyword(1) Generative Adversarial Networks  
Keyword(2) Image Generation  
Keyword(3) Deep Learning  
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1st Author's Name Yusuke Hara  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker
Date Time 2022-02-21 12:45:00 
Presentation Time 15 
Registration for IEICE-IE 
Paper #  
Volume (vol) 46 
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