Signal propagation in a gradient-based and evolutionary learning system

Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g. Lipizzaner, are empirically robust to them. The robustness arises from diversity that occurs by training populations of generators and discriminators in each cell of a toroidal grid. Communication, where signals in the form of parameters of the best GAN in a cell propagate in four directions: North, South, West and East, also plays a role, by communicating adaptations that are both new and fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner, except that it uses a different spatial topology, i.e. a ring. Our central question is whether the different directionality of signal propagation (effectively migration to one or more neighbors on each side of a cell) meets or exceeds the performance quality and training efficiency of Lipizzaner. Experimental analysis on different datasets (i.e, MNIST, CelebA, and COVID-19 chest X-ray images) shows that there are no significant differences between the performances of the trained generative models by both methods. However, Lipi-Ring significantly reduces the computational time (14.2%... 41.2%). Thus, Lipi-Ring offers an alternative to Lipizzaner when the computational cost of training matters. © 2021 Owner/Author.

Parallel/Distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images

This article presents an approach using parallel/distributed generative adversarial networks for image data augmentation, applied to generate COVID-19 training samples for computational intelligence methods. This is a relevant problem nowadays, considering the recent COVID-19 pandemic. Computational intelligence and learning methods are useful tools to assist physicians in the process of diagnosing diseases and acquire valuable medical knowledge. A specific generative adversarial network approach trained using a co-evolutionary algorithm is implemented, including a three-level parallel approach combining distributed memory and fine-grained parallelization using CPU and GPU. The experimental evaluation of the proposed method was performed on the high performance computing infrastructure provided by National Supercomputing Center, Uruguay. The main experimental results indicate that the proposed model is able to generate accurate images and the 3 × 3 version of the distributed GAN has better robustness properties of its training process, allowing to generate better and more diverse images. © 2021, Springer Nature Switzerland AG.