Exploring the Evolution of GANs through Quality Diversity

genetic and evolutionary computation conference, pp. 297-305, 2020.

Cited by: 0|Bibtex|Views8|DOI:https://doi.org/10.1145/3377930.3389824
Other Links: arxiv.org|academic.microsoft.com

Abstract:

Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, GANs are hard to train, and several aspects of the model should be previously designed by hand to ensure training success. In this context, evolutionary algori...More

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