This project aims to augment our theoretical understanding of the popular Generative Adversarial Nets (GANs). To achieve that, we will work in both the perspective of games and known non-convex optimization techniques.
GANs have the potential of affecting several significant tasks, such as domain adaptation or reinforcement learning, however at the moment one substantial limitation is the lack of their theoretical understanding. Through the proposed project, we aim to be able to use GANs as a prior to make neural networks robust to adversarial attacks. This project is interdisciplinary and aims at bridging the theoretical understanding of GANs with their stellar empirical performance using techniques from machine learning, optimization, game theory.