Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Beyond Local Nash Equilibria for Adversarial Networks Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise van der Pol, and Roderich Gross. Beyond Local Nash Equilibria for Adversarial Networks. ArXiv e-prints, June 2018. DownloadAbstractSave for some special cases, current training meth-ods for Generative Adversarial Networks (GANs)are at best guaranteed to converge to a `local Nashequilibrium' (LNE). Such LNEs, however, canbe arbitrarily far from an actual Nash equilibrium(NE), which implies that there are no guaranteeson the quality of the found generator or classifier.This paper proposes to model GANs explicitlyas finite games in mixed strategies, thereby en-suring that every LNE is an NE. With this for-mulation, we propose a solution method that isproven to monotonically converge to aresource-bounded Nash equilibrium (RB-NE): by increas-ing computational resources we can find bettersolutions. We empirically demonstrate that ourmethod is less prone to typical GAN problemssuch as mode collapse, and produces solutionsthat are less exploitable than those produced byGANs and MGANs, and closely resemble theo-retical predictions about NEs BibTeX Entry@article{Oliehoek18arxiv__beyond, author = {Frans A. Oliehoek and Rahul Savani and Jose Gallego{-}Posada and Elise van der Pol and Roderich Gross}, title = {Beyond Local {Nash} Equilibria for Adversarial Networks}, journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1806.07268}, primaryClass = "cs.LG", keywords = {Computer Science - Learning, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning}, year = 2018, month = jun, wwwnote = {Also available from <a href="https://arxiv.org/abs/1806.07268">arXiv</a>.}, keywords = {nonrefereed, arxiv}, abstract = { Save for some special cases, current training meth- ods 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 en- suring that every LNE is an NE. With this for- mulation, we propose a solution method that is proven to monotonically converge to a resource-bounded Nash equilibrium (RB-NE): by increas- ing computational resources we can find better solutions. We empirically demonstrate that our method is less prone to typical GAN problems such as mode collapse, and produces solutions that are less exploitable than those produced by GANs and MGANs, and closely resemble theo- retical predictions about NEs } }
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