Article ; Online: A Unifying Generator Loss Function for Generative Adversarial Networks.
2024 Volume 26, Issue 4
Abstract: A unifying α-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN) that uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system. The ... ...
| Abstract | A unifying α-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN) that uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system. The generator loss function is based on a symmetric class probability estimation type function, Lα, and the resulting GAN system is termed Lα-GAN. Under an optimal discriminator, it is shown that the generator's optimization problem consists of minimizing a Jensen-fα-divergence, a natural generalization of the Jensen-Shannon divergence, where fα is a convex function expressed in terms of the loss function Lα. It is also demonstrated that this Lα-GAN problem recovers as special cases a number of GAN problems in the literature, including VanillaGAN, least squares GAN (LSGAN), least |
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| Language | English |
| Publishing date | 2024-03-27 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2014734-X |
| ISSN | 1099-4300 ; 1099-4300 |
| ISSN (online) | 1099-4300 |
| ISSN | 1099-4300 |
| DOI | 10.3390/e26040290 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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