Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

Amol Kapoor; Ben Adlam; Charles Weill

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

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arxiv 1910.14137 v1 pith:6DCJXPBJ submitted 2019-10-30 stat.ML cs.LG

Ben Adlam , Charles Weill , Amol Kapoor This is my paper · ORCID

classification stat.MLcs.LG

keywords discriminatorgeneratormodeloverfittingadversarialfindgenerativenetworks

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We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization. We find that the model capacity of the discriminator has a significant effect on the generator's model quality, and that the generator's poor performance coincides with the discriminator underfitting. Contrary to our expectations, we find that generators with large model capacities relative to the discriminator do not show evidence of overfitting on CIFAR10, CIFAR100, and CelebA.

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