  Generative adversarial networks (GANs) have been extremely effective in
approximating complex distributions of high-dimensional, input data samples,
and substantial progress has been made in understanding and improving GAN
performance in terms of both theory and application. However, we currently lack
quantitative methods for model assessment. Because of this, while many GAN
variants are being proposed, we have relatively little understanding of their
relative abilities. In this paper, we evaluate the performance of various types
of GANs using divergence and distance functions typically used only for
training. We observe consistency across the various proposed metrics and,
interestingly, the test-time metrics do not favour networks that use the same
training-time criterion. We also compare the proposed metrics to human
perceptual scores.
