Realizing GANs via a Tunable Loss Function

We introduce a tunable GAN, called $alpha$-GAN, parameterized by $alpha in (0,infty]$, which interpolates between various $f$-GANs and Integral Probability Metric based GANs (under constrained discriminator set). We construct $alpha$-GAN using a supervised loss function, namely, $alpha$-loss, which is a tunable loss function capturing several canonical losses. We show that $alpha$-GAN is intimately related to the Arimoto divergence, which was first proposed by {O}sterriecher (1996), and later studied by Liese and Vajda (2006). We posit that the holistic understanding that $alpha$-GAN introduces will have practical benefits of addressing both the issues of vanishing gradients and mode collapse.