Improved evolution of generative adversarial networks

Abstract

Generative adversarial networks (GANs) achieved relevant results regarding the production of realistic samples. However, the training of GANs has issues that affect the stability and convergence of the algorithm. One line of research to tackle these issues uses Evolutionary Algorithms to drive the training and evolution of GANs, such as COEGAN. In this work, we propose COEGAN-v2, an extension of COEGAN that allows the use of spectral normalization and upsampling layers through the variation operators. Additionally, we use the loss functions of RaSGAN in training and also as fitness. Results show that COEGAN-v2 is more efficient and achieves better outcome quality when compared to the original COEGAN version and also regular GANs.