  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.
