Art

J-GLOBAL ID：202002226402498142   Reference number：20A0889926

Generative Adversarial Networks Handling Multiple Distances between Probability Distributions

複数の分布間距離を扱うGenerative Adversarial Networks

Author (3)： HIDAI SHIN'YA (東京都市大 知識工) ,  NAKANO HIDEHIRO (東京都市大 知識工) ,  MIYAUCHI ARATA (東京都市大 知識工)
Material： 電子情報通信学会技術研究報告  (IEICE Technical Report (Institute of Electronics, Information and Communication Engineers))
Volume： 119  Issue： 485(CCS2019 34-40)  Page： 21-24  Publication year： Mar. 18, 2020 
JST Material Number： S0532B  ISSN： 0913-5685  Document type： Proceedings
Article type： 原著論文  Country of issue： Japan (JPN)  Language： JAPANESE (JA)

Thesaurus term： *neural network (biomodel) ,...
Semi thesaurus term： Training algorithm ,...

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JST classification (1)： Brain and nervous system models  (EL02050C)

Reference (4)：

• Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville and Yoshua Bengio. Generative Adversarial Networks, 2014; arXiv:1406.2661.
• Martin Arjovsky, Soumith Chintala, Léon Bottou. Wasserstein GAN, 2017; arXiv:1701.07875.
• Marc G. Bellemare, No Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos. The Cramer Distance as a Solution to Biased Wasserstein Gradients, 2017; arXiv:1705.10743.
• Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford and Xi Chen. Improved Techniques for Training GANs, 2016; arXiv:1606.03498.

Terms in the title (1)： 分布