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arXiv · 1802.05411

Selecting the Best in GANs Family: a Post Selection Inference Framework

Yao-Hung Hubert Tsai·Makoto Yamada·Denny Wu·Ruslan Salakhutdinov·Ichiro Takeuchi·Kenji Fukumizu
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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 $\mathrm{MMD}_{inc}$ to measure the distribution discrepancy between generated and real images. $\mathrm{MMD}_{inc}$ enjoys the advantages of asymptotic normality, computation efficiency, and model agnosticity. We then propose a GANs analysis framework to select and test the "best" member in GANs family using the Post Selection Inference (PSI) with $\mathrm{MMD}_{inc}$. In the experiments, we adopt the proposed framework on 7 GANs variants and compare their $\mathrm{MMD}_{inc}$ scores.

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Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu. 2018-02-15. Selecting the Best in GANs Family: a Post Selection Inference Framework. https://arxiv.org/abs/1802.05411

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