{"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/stargan-v2-diverse-image-synthesis-for","title":"StarGAN v2: Diverse Image Synthesis for Multiple Domains","arxiv_id":"1912.01865","date":"2019-12-04","proceeding":"CVPR 2020 6","authors":["Yunjey Choi","Youngjung Uh","Jaejun Yoo","Jung-Woo Ha"],"abstract":"A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. 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