Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

• URL: http://arxiv.org/abs/2106.13590v1
• Date: Fri, 25 Jun 2021 12:40:36 GMT
• Title: Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks
• Authors: Jamal Toutouh and Erik Hemberg and Una-May O'Reilly
• Abstract summary: 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.
• Score: 10.603020431394157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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