{"type":"ScholarlyArticle","title":"Generative Warfare Nets: Ensemble via Adversaries and Collaborators","authors":[{"name":"Honglun Zhang","slug":"honglun-zhang","given":"Honglun","family":"Zhang","affiliation":"State Key Lab of Advanced Optical Communication System and Network , Shanghai Jiao Tong University; Artificial Intelligence Institute , Shanghai Jiao Tong University"},{"name":"Liqiang Xiao","slug":"liqiang-xiao","given":"Liqiang","family":"Xiao","affiliation":"State Key Lab of Advanced Optical Communication System and Network , Shanghai Jiao Tong University; Artificial Intelligence Institute , Shanghai Jiao Tong University"},{"name":"Wenqing Chen","slug":"wenqing-chen","given":"Wenqing","family":"Chen","affiliation":"State Key Lab of Advanced Optical Communication System and Network , Shanghai Jiao Tong University; Artificial Intelligence Institute , Shanghai Jiao Tong University"},{"name":"Yongkun Wang","slug":"yongkun-wang","given":"Yongkun","family":"Wang","affiliation":"Network and Information Center , Shanghai Jiao Tong University"},{"name":"Yaohui Jin","slug":"yaohui-jin","given":"Yaohui","family":"Jin","affiliation":"State Key Lab of Advanced Optical Communication System and Network , Shanghai Jiao Tong University; Artificial Intelligence Institute , Shanghai Jiao Tong University"}],"abstract":"Generative Adversarial Nets are a powerful method for training generative models of complex data, where a Generator and a Discriminator confront with each other and get optimized in a two-player minmax manner. In this paper, we propose the Generative Warfare Nets (GWN) that involve multiple generators and multiple discriminators from two sides to exploit the advantages of Ensemble Learning. We maintain the authorities for the generators and the discriminators to enhance inter-side interactions, and utilize the mechanisms of imitation and innovation to model intra-side interactions among the generators, where they can not only learn from but also compete with each other. Extensive experiments on three natural image datasets show that GWN can achieve state-of-the-art Inception scores and produce diverse high-quality synthetic results.","doi":"10.24963/ijcai.2018/427","url":"https://ijcai.org/proceedings/2018/427","pdf":"https://ijcai.org/proceedings/2018/0427.pdf","markdown":"https://ijcai.org/proceedings/2018/427.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/427","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":427,"pages":{"first":3075,"last":3081},"keywords":["Computer Vision: Computer Vision","Machine Learning: Ensemble Methods","Machine Learning: Learning Generative Models"],"videos":[],"references":[{"position":1,"raw":"[Arjovsky and Bottou, 2017] Martín Arjovsky and Léon Bottou. 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