{"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/jointgan-multi-domain-joint-distribution","title":"JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets","arxiv_id":"1806.02978","date":"2018-06-08","proceeding":"ICML 2018 7","authors":["Yunchen Pu","Shuyang Dai","Zhe Gan","Wei-Yao Wang","Guoyin Wang","Yizhe Zhang","Ricardo Henao","Lawrence Carin"],"abstract":"A new generative adversarial network is developed for joint distribution\nmatching. Distinct from most existing approaches, that only learn conditional\ndistributions, the proposed model aims to learn a joint distribution of\nmultiple random variables (domains). This is achieved by learning to sample\nfrom conditional distributions between the domains, while simultaneously\nlearning to sample from the marginals of each individual domain. The proposed\nframework consists of multiple generators and a single softmax-based critic,\nall jointly trained via adversarial learning. From a simple noise source, the\nproposed framework allows synthesis of draws from the marginals, conditional\ndraws given observations from a subset of random variables, or complete draws\nfrom the full joint distribution. Most examples considered are for joint\nanalysis of two domains, with examples for three domains also presented.","url_abs":"http://arxiv.org/abs/1806.02978v1","url_pdf":"http://arxiv.org/pdf/1806.02978v1.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":"jointgan-multi-domain-joint-distribution","repo_url":"https://github.com/sdai654416/Joint-GAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"jointgan-multi-domain-joint-distribution","repo_url":"https://github.com/mathcbc/jointGAN_toydata","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02978","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}