  We introduce the Metropolis-Hastings generative adversarial network (MH-GAN),
which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws
samples from the distribution implicitly defined by a GAN's
discriminator-generator pair, as opposed to standard GANs which draw samples
from the distribution defined only by the generator. It uses the discriminator
from GAN training to build a wrapper around the generator for improved
sampling. With a perfect discriminator, this wrapped generator samples from the
true distribution on the data exactly even when the generator is imperfect. We
demonstrate the benefits of the improved generator on multiple benchmark
datasets, including CIFAR-10 and CelebA, using the DCGAN, WGAN, and progressive
GAN.
