Title: Variance Regularizing Adversarial Learning,
Abstract: We introduce a novel approach for training adversarial models by replacing
the discriminator score with a bi-modal Gaussian distribution over the
real/fake indicator variables. In order to do this, we train the Gaussian
classifier to match the target bi-modal distribution implicitly through
meta-adversarial training. We hypothesize that this approach ensures a non-zero
gradient to the generator, even in the limit of a perfect classifier. We test
our method against standard benchmark image datasets as well as show the
classifier output distribution is smooth and has overlap between the real and
fake modes.