Reviews: f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Technical quality I am currently on the fence with respect to technical quality, but hope the authors can clarify the following in the rebuttal. The starting point for the method is a divergence D_f(P Q) which we aim to minimize. Unfortunately, the mini-max objective function of Eq. (6) is a lower-bound on this divergence. This seems problematic as optimizing Eq (6) would then not guarantee anything with respect to the original divergence, regardless of how tight the bound is. This is in stark contrast to variational EM, which maximizes a lower-bound on the log-likelihood, a quantity we also aim to maximize.