BriefGPT.xyz
Jun, 2018
GAN的判别器可近似性意味着多样性
Approximability of Discriminators Implies Diversity in GANs
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Yu Bai, Tengyu Ma, Andrej Risteski
TL;DR
本文研究发现,通过针对特定生成器选择具有强鉴别能力的鉴别器以学习Wasserstein距离下(或者在很多情况下是KL散度下)的分布,一定能以多项式复杂度学习,从而解决了GANs过于简单导致模式严重缺失的问题。
Abstract
While
generative adversarial networks
(GANs) have empirically produced impressive results on learning complex real-world distributions, recent work has shown that they suffer from lack of diversity or
mode collapse
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