{"type":"ScholarlyArticle","title":"MIXGAN: Learning Concepts from Different Domains for Mixture Generation","authors":[{"name":"Guang-Yuan Hao","slug":"guang-yuan-hao","given":"Guang-Yuan","family":"Hao","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China"},{"name":"Hong-Xing Yu","slug":"hong-xing-yu","given":"Hong-Xing","family":"Yu","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Wei-Shi Zheng","slug":"wei-shi-zheng","given":"Wei-Shi","family":"Zheng","affiliation":"School of Data and Computer Science , Sun Yat-sen University , China; Key Laboratory of Machine Intelligence and Advanced Computing , Ministry of Education , China; Collaborative Innovation Center of High Performance Computing , NUDT , China"}],"abstract":"In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-the-shelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models.","doi":"10.24963/ijcai.2018/306","url":"https://ijcai.org/proceedings/2018/306","pdf":"https://ijcai.org/proceedings/2018/0306.pdf","markdown":"https://ijcai.org/proceedings/2018/306.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/306","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":306,"pages":{"first":2212,"last":2219},"keywords":["Machine Learning: Deep Learning","Machine Learning: Unsupervised Learning","Machine Learning: Learning Generative Models"],"videos":[],"references":[{"position":1,"raw":"[Berthelot et al., 2017] David Berthelot, Tom Schumm, and Luke Metz. 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