  We prove a two-way correspondence between the min-max optimization of general
CPE loss function GANs and the minimization of associated $f$-divergences. We
then focus on $\alpha$-GAN, defined via the $\alpha$-loss, which interpolates
several GANs (Hellinger, vanilla, Total Variation) and corresponds to the
minimization of the Arimoto divergence. We show that the Arimoto divergences
induced by $\alpha$-GAN equivalently converge, for all $\alpha\in
\mathbb{R}_{>0}\cup\{\infty\}$. However, under restricted learning models and
finite samples, we provide estimation bounds which indicate diverse GAN
behavior as a function of $\alpha$. Finally, we present empirical results on a
toy dataset that highlight the practical utility of tuning the $\alpha$
hyperparameter.
