Presentation 2018-03-26
Suppression Method of Mode Collapse in Generative Adversarial Nets
Shinya Hidai, Hidehiro Nakano, Arata Miyauchi,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Generative Adversarial Nets (GAN) is constituted by two neural networks, Generator and Discrminator. Generator creates data, and Discriminator estimates data that came from training data. The purpose of GAN is to let Generator estimate the probability distribution of training data. There is a problem in the GAN that the Generator can only estimate the local maximum of a part of the probability distribution of the training data, which is known as Mode Collapse. In this paper, in order to suppress the occurrence of Mode Collapse, we propose a method to change loss function of Generator to an expression with a large gradient even when learning of Generator proceeds. We verify this proposed method by estimating the Gaussian mixture model in two dimensional space.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Generative Adversarial Nets / Mode Collapse / probability distribution / loss function
Paper # CCS2017-33
Date of Issue 2018-03-19 (CCS)

Conference Information
Committee CCS
Conference Date 2018/3/26(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Tokyo Univ. of Sci. (Morito Memorial Hall)
Topics (in Japanese) (See Japanese page)
Topics (in English) Natural Computing, etc.
Chair Naoki Wakamiya(Osaka Univ.)
Vice Chair Mikio Hasegawa(Tokyo Univ. of Science) / Makoto Naruse(NICT)
Secretary Mikio Hasegawa(Osaka Univ.) / Makoto Naruse(Tokyo City Univ.)
Assistant Chisa Takano(Hirishima City Univ.) / Takashi Shimada(Univ. of Tokyo) / Tomoya Suzuki(Ibaraki Univ.) / Ryo Takahashi(AUT)

Paper Information
Registration To Technical Committee on Complex Communication Sciences
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Suppression Method of Mode Collapse in Generative Adversarial Nets
Sub Title (in English)
Keyword(1) Generative Adversarial Nets
Keyword(2) Mode Collapse
Keyword(3) probability distribution
Keyword(4) loss function
1st Author's Name Shinya Hidai
1st Author's Affiliation Tokyo City University(Tokyo City Univ.)
2nd Author's Name Hidehiro Nakano
2nd Author's Affiliation Tokyo City University(Tokyo City Univ.)
3rd Author's Name Arata Miyauchi
3rd Author's Affiliation Tokyo City University(Tokyo City Univ.)
Date 2018-03-26
Paper # CCS2017-33
Volume (vol) vol.117
Number (no) CCS-520
Page pp.pp.1-6(CCS),
#Pages 6
Date of Issue 2018-03-19 (CCS)