Variance Regularizing Adversarial Learning

Karan Grewal; R Devon Hjelm; Yoshua Bengio

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arXiv · 1707.00309

Variance Regularizing Adversarial Learning

Karan Grewal·R Devon Hjelm·Yoshua Bengio

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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.

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BibTeX RIS

Karan Grewal, R Devon Hjelm, Yoshua Bengio. 2018-08-19. Variance Regularizing Adversarial Learning. https://arxiv.org/abs/1707.00309

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