{"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/nips-2016-tutorial-generative-adversarial","title":"NIPS 2016 Tutorial: Generative Adversarial Networks","arxiv_id":"1701.00160","date":"2016-12-31","proceeding":null,"authors":["Ian Goodfellow"],"abstract":"This report summarizes the tutorial presented by the author at NIPS 2016 on\ngenerative adversarial networks (GANs). The tutorial describes: (1) Why\ngenerative modeling is a topic worth studying, (2) how generative models work,\nand how GANs compare to other generative models, (3) the details of how GANs\nwork, (4) research frontiers in GANs, and (5) state-of-the-art image models\nthat combine GANs with other methods. Finally, the tutorial contains three\nexercises for readers to complete, and the solutions to these exercises.","url_abs":"http://arxiv.org/abs/1701.00160v4","url_pdf":"http://arxiv.org/pdf/1701.00160v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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