Evaluating GANs via Duality

Anonymous

Sep 27, 2018 (modified: Oct 10, 2018) ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem. This is especially troublesome given the lack of an evaluation metric that can reliably detect non-convergent behaviors. We leverage the notion of duality gap from game theory in order to propose a novel convergence metric for GANs that has low computational cost. We verify the validity of the proposed metric for various test scenarios commonly used in the literature.
  • Keywords: Generative Adversarial Networks, GANs, game theory
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