{"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/mixgan-learning-concepts-from-different","title":"MIXGAN: Learning Concepts from Different Domains for Mixture Generation","arxiv_id":"1807.01659","date":"2018-07-04","proceeding":null,"authors":["Guang-Yuan Hao","Hong-Xing Yu","Wei-Shi Zheng"],"abstract":"In this work, we present an interesting attempt on mixture generation:\nabsorbing different image concepts (e.g., content and style) from different\ndomains and thus generating a new domain with learned concepts. In particular,\nwe propose a mixture generative adversarial network (MIXGAN). MIXGAN learns\nconcepts of content and style from two domains respectively, and thus can join\nthem for mixture generation in a new domain, i.e., generating images with\ncontent from one domain and style from another. MIXGAN overcomes the limitation\nof current GAN-based models which either generate new images in the same domain\nas they observed in training stage, or require off-the-shelf content templates\nfor transferring or translation. Extensive experimental results demonstrate the\neffectiveness of MIXGAN as compared to related state-of-the-art GAN-based\nmodels.","url_abs":"http://arxiv.org/abs/1807.01659v1","url_pdf":"http://arxiv.org/pdf/1807.01659v1.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":"mixgan-learning-concepts-from-different","repo_url":"https://github.com/GuangyuanHao/MIXGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.01659","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}