Beyond Local Nash Equilibria for Adversarial Networks

18 Jun 2018Frans A. OliehoekRahul SavaniJose GallegoElise van der PolRoderich Groß

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... (read more)

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