Beyond Local Nash Equilibria for Adversarial Networks

Frans A Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol & Roderich GroƟ
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual Nash equilibrium (NE), which implies that there are no guarantees on the quality of the found generator or classifier. This paper proposes to model GANs explicitly as finite games in mixed strategies, thereby ensuring that every LNE is an NE....

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