Reviews: A Domain Agnostic Measure for Monitoring and Evaluating GANs

The idea of studying GANs from the game theory perspective is not new; however, using the duality gap as a performance metric (some sort of divergence between the generated data distribution and the real data distribution) is original to the best of my knowledge. The paper is written clearly. In terms of significance, while the idea of the duality gap is "natural" when considering the game theory perspective for GANs, it is not clear why this is a good metric for _any_ domain. The authors imply that it is a good idea to find a metric that does not depend on the domain of the data, but given all the parallels between GANs and the different divergences between probability distributions (JS, Wasserstein, etc.) I think the main problem is to find a metric that can be thought as correctly modeling the distance between high-dimensional datasets such as the ones given by images. In that case, modeling this aspect (which is highly domain-dependent) is crucial for understanding what a GAN is capturing about the data distribution.