{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fictitious-gan-training-gans-with-historical","title":"Fictitious GAN: Training GANs with Historical Models","arxiv_id":"1803.08647","date":"2018-03-23","proceeding":"ECCV 2018 9","authors":["Hao Ge","Yin Xia","Xu Chen","Randall Berry","Ying Wu"],"abstract":"Generative adversarial networks (GANs) are powerful tools for learning\ngenerative models. In practice, the training may suffer from lack of\nconvergence. GANs are commonly viewed as a two-player zero-sum game between two\nneural networks. Here, we leverage this game theoretic view to study the\nconvergence behavior of the training process. Inspired by the fictitious play\nlearning process, a novel training method, referred to as Fictitious GAN, is\nintroduced. Fictitious GAN trains the deep neural networks using a mixture of\nhistorical models. Specifically, the discriminator (resp. generator) is updated\naccording to the best-response to the mixture outputs from a sequence of\npreviously trained generators (resp. discriminators). It is shown that\nFictitious GAN can effectively resolve some convergence issues that cannot be\nresolved by the standard training approach. It is proved that asymptotically\nthe average of the generator outputs has the same distribution as the data\nsamples.","url_abs":"http://arxiv.org/abs/1803.08647v2","url_pdf":"http://arxiv.org/pdf/1803.08647v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fictitious-gan-training-gans-with-historical","repo_url":"https://github.com/pijel/fGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.08647","atlas_url":"https://app.syntology.ai/?focus=1803.08647","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}