Title: VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning,
Abstract: Deep generative models provide powerful tools for distributions over
complicated manifolds, such as those of natural images. But many of these
methods, including generative adversarial networks (GANs), can be difficult to
train, in part because they are prone to mode collapse, which means that they
characterize only a few modes of the true distribution. To address this, we
introduce VEEGAN, which features a reconstructor network, reversing the action
of the generator by mapping from data to noise. Our training objective retains
the original asymptotic consistency guarantee of GANs, and can be interpreted
as a novel autoencoder loss over the noise. In sharp contrast to a traditional
autoencoder over data points, VEEGAN does not require specifying a loss
function over the data, but rather only over the representations, which are
standard normal by assumption. On an extensive set of synthetic and real world
image datasets, VEEGAN indeed resists mode collapsing to a far greater extent
than other recent GAN variants, and produces more realistic samples.