Spatial Evolutionary Generative Adversarial Networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks

Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification

Funding for open access charge: Universidad de Málaga / CBU

COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.Comment: Published in GECCO 2019. arXiv admin note: text overlap with arXiv:1912.0617

Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks

Recent developments in Deep Learning are noteworthy when it comes to learning the probability distribution of points through neural networks, and one of the crucial parts for such progress is because of Generative Adversarial Networks (GANs). In GANs, two neural networks, Generator and Discriminator, compete amongst each other to learn the probability distribution of points in visual pictures. A lot of research has been conducted to overcome the challenges of GANs which include training instability, mode collapse and vanishing gradient. However, there was no significant proof found on whether modern techniques consistently outperform vanilla GANs, and it turns out that different advanced techniques distinctively perform on different datasets. In this thesis, we propose two neuroevolutionary training techniques for deep convolutional GANs. We evolve the deep GANs architecture in low data regime. Using Fréchet Inception Distance (FID) score as the fitness function, we select the best deep convolutional topography generated by the evolutionary algorithm. The parameters of the best-selected individuals are maintained throughout the generations, and we continue to train the population until individuals demonstrate convergence. We compare our approach with the Vanilla GANs, Deep Convolutional GANs and COEGAN. Our experiments show that an evolutionary algorithm-based training technique gives a lower FID score than those of benchmark models. A lower FID score results in better image quality and diversity in the generated images