# Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2106.13590 · HTML: https://mlsanity.com/paper/2106.13590/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 11 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [Learning What and Where to Draw](https://mlsanity.com/paper/1610.02454/) (2016)
- [Dual Motion GAN for Future-Flow Embedded Video Prediction](https://mlsanity.com/paper/1708.00284/) (2017)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [Triangle Generative Adversarial Networks](https://mlsanity.com/paper/1709.06548/) (2017)
- [Spatial Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1905.12702/) (2019)
- [Towards Distributed Coevolutionary GANs](https://mlsanity.com/paper/1807.08194/) (2018)
- [Data Dieting in GAN Training](https://mlsanity.com/paper/2004.04642/) (2020)
- [Lipizzaner: A System That Scales Robust Generative Adversarial Network Training](https://mlsanity.com/paper/1811.12843/) (2018)
- [Analyzing the Components of Distributed Coevolutionary GAN Training](https://mlsanity.com/paper/2008.01124/) (2020)

## Abstract (arXiv)

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.