Selecting the Best in GANs Family: a Post Selection Inference Framework
Machine Learning
2018-06-26 v2 Machine Learning
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 to measure the distribution discrepancy between generated and real images. 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 . In the experiments, we adopt the proposed framework on 7 GANs variants and compare their scores.
Cite
@article{arxiv.1802.05411,
title = {Selecting the Best in GANs Family: a Post Selection Inference Framework},
author = {Yao-Hung Hubert Tsai and Makoto Yamada and Denny Wu and Ruslan Salakhutdinov and Ichiro Takeuchi and Kenji Fukumizu},
journal= {arXiv preprint arXiv:1802.05411},
year = {2018}
}