Selecting the Best in GANs Family: a Post Selection Inference FrameworkDownload PDF

Feb 12, 2018 (edited Jun 04, 2018)ICLR 2018 Workshop SubmissionReaders: Everyone
  • Keywords: Post Selection Inference, GAN Evaluation
  • Abstract: "Which Generative Adversarial Networks (GANs) generates the most plausible images?" has been a frequently asked question among researchers. To address this problem, we first propose an \emph{incomplete} U-statistics estimate of maximum mean discrepancy $\textnormal{MMD}_{inc}$ to measure the distribution discrepancy between generated and real images. $\textnormal{MMD}_{inc}$ enjoys the advantages of asymptotic normality, computation efficiency, and model agnosticity. We then propose a GANs analysis framework to select the "best" member in GANs family using the Post Selection Inference (PSI) with $\textnormal{MMD}_{inc}$. In the experiments, we adopt the proposed framework on 7 GANs variants and compare their $\textnormal{MMD}_{inc}$ scores.
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