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arXiv · 2106.13590

Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

Jamal Toutouh·Erik Hemberg·Una-May O'Reilly
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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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Jamal Toutouh, Erik Hemberg, Una-May O'Reilly. 2021-06-25. Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks. https://arxiv.org/abs/2106.13590

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