Training bidirectional generative adversarial networks with hints

Abstract

The generative adversarial network (GAN) is composed of a generator and a discriminator where the generator is trained to transform random latent vectors to valid samples from a distribution and the discriminator is trained to separate such “fake” examples from true examples of the distribution, which in turn forces the generator to generate better fakes. The bidirectional GAN (BiGAN) also has an encoder working in the inverse direction of the generator to produce the latent space vector for a given example. This added encoder allows defining auxiliary reconstruction losses as hints for a better generator. On five widely-used data sets, we showed that BiGANs trained with the Wasserstein loss and augmented with hints learn better generators in terms of image generation quality and diversity, as measured numerically by the 1-nearest neighbor test, Fréchet inception distance, and reconstruction error, and qualitatively by visually analyzing the generated samples.