{"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/graphical-generative-adversarial-networks","title":"Graphical Generative Adversarial Networks","arxiv_id":"1804.03429","date":"2018-04-10","proceeding":"NeurIPS 2018 12","authors":["Chongxuan Li","Max Welling","Jun Zhu","Bo Zhang"],"abstract":"We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model\nstructured data. Graphical-GAN conjoins the power of Bayesian networks on\ncompactly representing the dependency structures among random variables and\nthat of generative adversarial networks on learning expressive dependency\nfunctions. We introduce a structured recognition model to infer the posterior\ndistribution of latent variables given observations. We generalize the\nExpectation Propagation (EP) algorithm to learn the generative model and\nrecognition model jointly. Finally, we present two important instances of\nGraphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN),\nwhich can successfully learn the discrete and temporal structures on visual\ndatasets, respectively.","url_abs":"http://arxiv.org/abs/1804.03429v2","url_pdf":"http://arxiv.org/pdf/1804.03429v2.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":"graphical-generative-adversarial-networks","repo_url":"https://github.com/zhenxuan00/graphical-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03429","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}