Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • GANGs: Generative Adversarial Network GamesFrans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise Van der Pol, Edwin D. De Jong, and Roderich Groß. GANGs: Generative Adversarial Network Games. ArXiv e-prints, December 2017. DownloadAbstractGenerative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling. As GANs are difficult to train much research has focused on this. However, very little of this research has directly exploited game-theoretic techniques. We introduce Generative Adversarial Network Games (GANGs), which explicitly model a finite zero-sum game between a generator (G) and classifier (C) that use mixed strategies. The size of these games precludes exact solution methods, therefore we define resource-bounded best responses (RBBRs), and a resource-bounded Nash Equilibrium (RB-NE) as a pair of mixed strategies such that neither G or C can find a better RBBR. The RB-NE solution concept is richer than the notion of `local Nash equilibria' in that it captures not only failures of escaping local optima of gradient descent, but applies to any approximate best response computations, including methods with random restarts. To validate our approach, we solve GANGs with the Parallel Nash Memory algorithm, which provably monotonically converges to an RB-NE. We compare our results to standard GAN setups, and demonstrate that our method deals well with typical GAN problems such as mode collapse, partial mode coverage and forgetting. BibTeX Entry@ARTICLE{Oliehoek17arXiv, author = {Oliehoek, Frans ~A. and Savani, Rahul and Gallego-Posada, Jos{e} and Van der Pol, Elise and De Jong, Edwin~D. and Gro{\ss}, Roderich}, title = "{GANGs: Generative Adversarial Network Games}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1712.00679}, primaryClass = "stat.ML", year = 2017, month = dec, url = {https://arxiv.org/abs/1712.00679}, keywords = {nonrefereed, arxiv}, abstract = {Generative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling. As GANs are difficult to train much research has focused on this. However, very little of this research has directly exploited game-theoretic techniques. We introduce Generative Adversarial Network Games (GANGs), which explicitly model a finite zero-sum game between a generator (G) and classifier (C) that use mixed strategies. The size of these games precludes exact solution methods, therefore we define resource-bounded best responses (RBBRs), and a resource-bounded Nash Equilibrium (RB-NE) as a pair of mixed strategies such that neither G or C can find a better RBBR. The RB-NE solution concept is richer than the notion of `local Nash equilibria' in that it captures not only failures of escaping local optima of gradient descent, but applies to any approximate best response computations, including methods with random restarts. To validate our approach, we solve GANGs with the Parallel Nash Memory algorithm, which provably monotonically converges to an RB-NE. We compare our results to standard GAN setups, and demonstrate that our method deals well with typical GAN problems such as mode collapse, partial mode coverage and forgetting.} }
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