  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.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning