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
overfitting. Finally, we use ideas from the one-shot learning literature to
develop a novel fast learning critic.