Parallel/distributed implementation of cellular training for generative adversarial neural networks

Generative adversarial networks (GANs) are widely used to learn generative\nmodels. GANs consist of two networks, a generator and a discriminator, that\napply adversarial learning to optimize their parameters. This article presents\na parallel/distributed implementation of a cellular competitive coevolutionary\nmethod to train two populations of GANs. A distributed memory parallel\nimplementation is proposed for execution in high performance/supercomputing\ncenters. Efficient results are reported on addressing the generation of\nhandwritten digits (MNIST dataset samples). Moreover, the proposed\nimplementation is able to reduce the training times and scale properly when\nconsidering different grid sizes for training.\n