Evaluating GANs via Duality

Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Nathanael Perraudin, Thomas Hofmann, Andreas Krause

Abstract: Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem. This is especially troublesome given the lack of an evaluation metric that can reliably detect non-convergent behaviors. We leverage the notion of duality gap from game theory in order to propose a novel convergence metric for GANs that has low computational cost. We verify the validity of the proposed metric for various test scenarios commonly used in the literature.

Keywords: Generative Adversarial Networks, GANs, game theory

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1811.05512/code)