Title: Out-of-Sample Testing for GANs,
Abstract: We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a
test set to directly measure the reconstruction quality in the original sample
space (no auxiliary networks are necessary), and it also computes the
(log)likelihood for the reconstructed samples in the test set. Further, EvalGAN
is agnostic to the GAN algorithm and the dataset. We decided to test it on
three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets.