  Generative adversarial networks (GANs) are one of the most popular methods
for generating images today. While impressive results have been validated by
visual inspection, a number of quantitative criteria have emerged only
recently. We argue here that the existing ones are insufficient and need to be
in adequation with the task at hand. In this paper we introduce two measures
based on image classification---GAN-train and GAN-test, which approximate the
recall (diversity) and precision (quality of the image) of GANs respectively.
We evaluate a number of recent GAN approaches based on these two measures and
demonstrate a clear difference in performance. Furthermore, we observe that the
increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet,
shows an inverse correlation with the quality of the GANs, as clearly evident
from our measures.
