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arXiv · 2006.14265

Empirical Analysis of Overfitting and Mode Drop in GAN Training

Yasin Yazici·Chuan-Sheng Foo·Stefan Winkler·Kim-Hui Yap·Vijay Chandrasekhar
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Abstract

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedure, GANs can overfit and exhibit almost no mode drop. Our results shed light on important characteristics of the GAN training procedure. They also provide evidence against prevailing intuitions that GANs do not memorize the training set, and that mode dropping is mainly due to properties of the GAN objective rather than how it is optimized during training.

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Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Vijay Chandrasekhar. 2020-06-25. Empirical Analysis of Overfitting and Mode Drop in GAN Training. https://arxiv.org/abs/2006.14265

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