Comparison of Maximum Likelihood and GAN-based training of Real NVPs
We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect over-fitting. Finally, we use ideas from the one-shot learning literature to develop a novel fast learning critic.