  Generative adversarial networks (GANs) can implicitly learn rich
distributions over images, audio, and data which are hard to model with an
explicit likelihood. We present a practical Bayesian formulation for
unsupervised and semi-supervised learning with GANs. Within this framework, we
use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of
the generator and discriminator networks. The resulting approach is
straightforward and obtains good performance without any standard interventions
such as feature matching, or mini-batch discrimination. By exploring an
expressive posterior over the parameters of the generator, the Bayesian GAN
avoids mode-collapse, produces interpretable and diverse candidate samples, and
provides state-of-the-art quantitative results for semi-supervised learning on
benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN,
Wasserstein GANs, and DCGAN ensembles.
