  In this technical report we describe some properties of f-divergences and
f-GAN training. We present an elementary derivation of the f-divergence lower
bounds which form the basis of f-GAN training. We derive informative but
perhaps underappreciated properties of f-divergences and f-GAN training,
including a gradient matching property and the fact that all f-divergences
agree up to an overall scale factor on the divergence between nearby
distributions. We provide detailed expressions for computing various common
f-divergences and their variational lower bounds. Finally, based on our
reformulation, we slightly generalize f-GAN training in a way that may improve
its stability.
