{"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/from-gan-to-wgan","title":"From GAN to WGAN","arxiv_id":"1904.08994","date":"2019-04-18","proceeding":null,"authors":["Lilian Weng"],"abstract":"This paper explains the math behind a generative adversarial network (GAN)\nmodel and why it is hard to be trained. Wasserstein GAN is intended to improve\nGANs' training by adopting a smooth metric for measuring the distance between\ntwo probability distributions.","url_abs":"http://arxiv.org/abs/1904.08994v1","url_pdf":"http://arxiv.org/pdf/1904.08994v1.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":"from-gan-to-wgan","repo_url":"https://github.com/Sinestro38/qosf-qgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"math","task_name":"Math"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.08994","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}