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arXiv · 2106.05232

Realizing GANs via a Tunable Loss Function

Gowtham R. Kurri·Tyler Sypherd·Lalitha Sankar
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Abstract

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

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Gowtham R. Kurri, Tyler Sypherd, Lalitha Sankar. 2021-10-18. Realizing GANs via a Tunable Loss Function. https://arxiv.org/abs/2106.05232

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