{"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/towards-efficient-and-unbiased-implementation","title":"Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs","arxiv_id":"1904.01184","date":"2019-04-02","proceeding":null,"authors":["Zhiming Zhou","Jian Shen","Yuxuan Song","Wei-Nan Zhang","Yong Yu"],"abstract":"Lipschitz continuity recently becomes popular in generative adversarial\nnetworks (GANs). It was observed that the Lipschitz regularized discriminator\nleads to improved training stability and sample quality. The mainstream\nimplementations of Lipschitz continuity include gradient penalty and spectral\nnormalization. In this paper, we demonstrate that gradient penalty introduces\nundesired bias, while spectral normalization might be over restrictive. We\naccordingly propose a new method which is efficient and unbiased. Our\nexperiments verify our analysis and show that the proposed method is able to\nachieve successful training in various situations where gradient penalty and\nspectral normalization fail.","url_abs":"http://arxiv.org/abs/1904.01184v1","url_pdf":"http://arxiv.org/pdf/1904.01184v1.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":"towards-efficient-and-unbiased-implementation","repo_url":"https://github.com/ZhimingZhou/AdaShift-Lipschitz-GANs-MaxGP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"spectral-normalization","method_name":"Spectral Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}