• Corpus ID: 28214862

A Quantitative Measure of Generative Adversarial Network Distributions

  title={A Quantitative Measure of Generative Adversarial Network Distributions},
  author={Dan Hendrycks and Steven Basart},
We introduce a new measure for evaluating the quality of distributions learned by Generative Adversarial Networks (GANs). This measure computes the KullbackLeibler divergence from a GAN-generated image set to a real image set. Since our measure utilizes a GAN’s whole distribution, our measure penalizes outputs lacking in diversity, and it contrasts with evaluating GANs based upon a few cherrypicked examples. We demonstrate the measure’s efficacy on the MNIST, SVHN, and CIFAR-10 datasets. 

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