In recent years, machine learning with Generative Adversarial Networks (GANs) has been recognized as a powerful method for generative modeling. Generative modeling is the problem of estimating the underlying distribution of a set of samples. GANs accomplish this using unsupervised learning. They have also been extended to handle semi-supervised and fully supervised learning paradigms. GANs have been successfully applied to many domains. They can generate novel images (e.g., image colorization or super-resolution, photograph editing, and text-to-image translation), sound (e.g., voice translation and music generation), and video (e.g., video-to-video translation, deep fakes generation, and AI-assisted video calls), finding application in domains of multimedia information, engineering, science, design, art, and games.

GANs are an adversarial paradigm. Two NNs compete with each other using antagonistic lost function to train the parameters with gradient descent. This connects them to evolution because evolution also exhibits adversarial engagements and competitive coevolution. In fact, the evolutionary computation community’s study of coevolutionary pathologies and its work on competitive and cooperative coevolutionary algorithms offers a means of solving convergence impasses often encountered in GAN training.

In this tutorial we will explain:
(a) The main concepts of generative modeling and adversarial learning.
(b) GAN gradient-based training and the main pathologies that prevent ideal convergence. Specifically, we will explain mode collapse, oscillation, and vanishing gradients.
(c) Coevolutionary algorithms and how they can be applied to train GANs. Specifically, we will explain how algorithm enhancements address non-ideal convergence
(d) To demonstrate we will draw upon the open-source Lipizzaner framework (url: http://lipizzaner.csail.mit.edu/). This framework is easy to use and extend. It sets up a spatial grid of communicating populations of GANs.
(e) Students will be given the opportunity to set up and use the Lipizzaner framework during the tutorial by means of a jupyter notebook expressly developed for teaching purposes.