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Deep generative models provide powerful tools for distributions overcomplicated manifolds, such as those of natural images. But many of thesemethods, including generative adversarial networks (GANs), can be difficult totrain, in part because they are prone to mode collapse, which means that theycharacterize only a few modes of the true distribution. To address this, weintroduce VEEGAN, which features a reconstructor network, reversing the actionof the generator by mapping from data to noise. Our training objective retainsthe original asymptotic consistency guarantee of GANs, and can be interpretedas a novel autoencoder loss over the noise. In sharp contrast to a traditionalautoencoder over data points, VEEGAN does not require specifying a lossfunction over the data, but rather only over the representations, which arestandard normal by assumption. On an extensive set of synthetic and real worldimage datasets, VEEGAN indeed resists mode collapsing to a far greater extentthan other recent GAN variants, and produces more realistic samples.

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning