Graphical Generative Adversarial Networks

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We generalize the Expectation Propagation (EP) algorithm to learn the generative model and recognition model jointly. Finally, we present two important instances of Graphical-GAN, i. e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN), which can successfully learn the discrete and temporal structures on visual datasets, respectively.

Authors

• Chongxuan Li
• Max Welling
• Jun Zhu Tsinghua University National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics
• Bo Zhang Zhejiang University

Keywords

No keywords are indexed for this paper.