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arXiv · 2205.06393

$\alpha$-GAN: Convergence and Estimation Guarantees

Gowtham R. Kurri·Monica Welfert·Tyler Sypherd·Lalitha Sankar
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Abstract

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.

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Gowtham R. Kurri, Monica Welfert, Tyler Sypherd, Lalitha Sankar. 2022-05-12. $\alpha$-GAN: Convergence and Estimation Guarantees. https://arxiv.org/abs/2205.06393

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