Generative Warfare Nets: Ensemble via Adversaries and Collaborators

Generative Warfare Nets: Ensemble via Adversaries and Collaborators

Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3075-3081. https://doi.org/10.24963/ijcai.2018/427

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.
Keywords:
Machine Learning: Ensemble Methods
Machine Learning: Learning Generative Models
Computer Vision: Computer Vision