text: The paper takes an interesting approach to solve the existing problems of GAN training, using Coulomb potential for addressing the learning problem.  It is also well written with a clear presentation of the motivation of the problems it is trying to address, the background and proves the optimality of the suggested solution.  My understanding and validity of the proof is still an educated guess.  I have been through section A.2 , but I'm unfamiliar with the earlier literature on the similar topics so I would not be able to comment on it.  Overall, I think this is a good paper that provides a novel way of looking at and solving problems in GANs.  I just had a couple of points in the paper that I would like some clarification on :  * In section 2.2.1 : The notion of the generated a_i not disappearing is something I did not follow.  What does it mean for a generated sample to "not disappear" ?  and this directly extends to the continuity equation in (2).  * In section 1 : in the explanation of the 3rd problem that GANs exhibit i.e.  the generator not being able to generalize the distribution of the input samples, I was hoping if you could give a bit more motivation as to why this happens.  I don't think this needs to be included in the paper,  but would like to have it for a personal clarification.