  Generative adversarial networks (GANs) are successful deep generative models.
GANs are based on a two-player minimax game. However, the objective function
derived in the original motivation is changed to obtain stronger gradients when
learning the generator. We propose a novel algorithm that repeats the density
ratio estimation and f-divergence minimization. Our algorithm offers a new
perspective toward the understanding of GANs and is able to make use of
multiple viewpoints obtained in the research of density ratio estimation, e.g.
what divergence is stable and relative density ratio is useful.
