# Implicit competitive regularization in GANs

@article{Schfer2020ImplicitCR, title={Implicit competitive regularization in GANs}, author={Florian Sch{\"a}fer and Hongkai Zheng and Anima Anandkumar}, journal={ArXiv}, year={2020}, volume={abs/1910.05852} }

To improve the stability of GAN training we need to understand why they can produce realistic samples. Presently, this is attributed to properties of the divergence obtained under an optimal discriminator. This argument has a fundamental flaw: If we do not impose regularity of the discriminator, it can exploit visually imperceptible errors of the generator to always achieve the maximal generator loss. In practice, gradient penalties are used to regularize the discriminator. However, this needs…

## 25 Citations

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