{"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/stylegan2-distillation-for-feed-forward-image","title":"StyleGAN2 Distillation for Feed-forward Image Manipulation","arxiv_id":"2003.03581","date":"2020-03-07","proceeding":"ECCV 2020 8","authors":["Yuri Viazovetskyi","Vladimir Ivashkin","Evgeny Kashin"],"abstract":"StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was explicitly trained to have disentangled directions in latent space, which allows efficient image manipulation by varying latent factors. 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