Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

Abstract

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial interactions. Co-evolutionary GAN (CoE-GAN) training algorithms have shown to be resilient to these pathologies. This article introduces Mustangs, a spatially distributed CoE-GAN, which fosters diversity by using different loss functions during the training. Experimental analysis on MNIST and CelebA demonstrated that Mustangs trains statistically more accurate generators.

Accepted to be presented during Conference of the Spanish Association of Artificial Intelligence (CAEPIA 2021). arXiv admin note: substantial text overlap with arXiv:1905.12702

References in corpus (3)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Learning What and Where to Draw
• Spatial Evolutionary Generative Adversarial Networks