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 nonzero 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.
Using a Gaussian classifier over the real/fake indicator variables has also been shown to have a smoothing effect on the discriminator function REF .
Variance Regularizing Adversarial Learning
ArXiv
ArXiv
Mathematics Computer Science