Realizing GANs via a Tunable Loss Function

Gowtham R. Kurri, Tyler Sypherd, Lalitha Sankar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We introduce a tunable GAN, called α-GAN, parameterized by α(0, ∞], 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 posit that the holistic understanding that α- GAN introduces will have practical benefits of addressing both the issues of vanishing gradients and mode collapses.

Original languageEnglish (US)
Title of host publication2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665403122
StatePublished - 2021
Event2021 IEEE Information Theory Workshop, ITW 2021 - Virtual, Online, Japan
Duration: Oct 17 2021Oct 21 2021

Publication series

Name2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings


Conference2021 IEEE Information Theory Workshop, ITW 2021
CityVirtual, Online

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software


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