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, Elise van der Pol, and Roderich Groß. Beyond Local Nash Equilibria for Adversarial Networks. In Artificial Intelligence, pp. 73–89, Springer International Publishing, September 2019. DownloadAbstractSave 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. We use the Parallel Nash Memory as a solution method, which is proven to monotonically converge to a resource-bounded Nash equilibrium. 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. BibTeX Entry@inproceedings{Oliehoek19BNAIC_pp, author= {Oliehoek, Frans A. and Savani, Rahul and Gallego, Jose and van der Pol, Elise and Gro{\ss}, Roderich}, editor= {Atzmueller, Martin and Duivesteijn, Wouter}, title= {Beyond Local {Nash} Equilibria for Adversarial Networks}, booktitle= {Artificial Intelligence}, year= {2019}, publisher= {Springer International Publishing}, pages= {73--89}, month = sep, OPTurl = {https://doi.org/10.1007/978-3-030-31978-6_7}, doi = {10.1007/978-3-030-31978-6\_7}, wwwnote = {Also see <a href="https://arxiv.org/abs/1806.07268">arXiv version</a>.}, keywords = {refereed}, abstract = { 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. We use the Parallel Nash Memory as a solution method, which is proven to monotonically converge to a resource-bounded Nash equilibrium. 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. } }
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