Paper:
Published as a conference paper at ICLR 2018 MANY PATHS TO EQUILIBRIUM: GANS DO NOT NEED TO DECREASE A DIVERGENCE AT EVERY STEP
Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.

Review:
This paper answers recent critiques about ``standard GAN'' that were recently formulated to motivate variants based on other losses, in particular using ideas from optimal transport.  It makes main points
1) ``standard GAN'' is an ill-defined term that may refer to two different learning criteria, with different properties
2) though the non-saturating variant (see Eq. 3) of ``standard GAN'' may converge towards a minimum of the Jensen-Shannon divergence, it does not mean that the minimization process follows gradients of the Jensen-Shannon divergence (and conversely, following gradient paths of the Jensen-Shannon divergence may not converge towards a minimum, but this was rather the point of the previous critiques about ``standard GAN''). 
3) the penalization strategies introduced for ``non-standard GAN'' with specific motivations, may also apply successfully to the ``standard GAN'', improving robustness, thereby helping to set hyperparameters.
Note that item 2) is relevant in many other setups in the deep learning framework and is often overlooked.
Overall, I believe that the paper provides enough material to substantiate these claims, even if the message could be better delivered. In particular, the writing is sometimes ambiguous (e.g. in Section 2.3, the reader who did not follow the recent developments on the subject on arXiv will have difficulties to rebuild the cross-references between authors, acronyms and formulae). The answers to the critiques referenced in the 
 paper are convincing, though I must admit that I don't know how crucial it is to answer these critics, since it is difficult to assess wether they reached or will reach a large audience.
Details:
- p. 4 please do not qualify KL as a distance metric 
- Section 4.3: "Every GAN variant was trained for 200000 iterations, and 5 discriminator updates were done for each generator update" is ambiguous: what is exactly meant by "iteration" (and sometimes step elsewhere)? 
- Section 4.3: the performance measure is not relevant regarding distributions. The l2 distance is somewhat OK for means, but it makes little sense for covariance matrices.