June 6, 2024, 4:45 a.m. | Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu

cs.LG updates on arXiv.org arxiv.org

arXiv:2305.13517v3 Announce Type: replace-cross
Abstract: Group-invariant generative adversarial networks (GANs) are a type of GANs in which the generators and discriminators are hardwired with group symmetries. Empirical studies have shown that these networks are capable of learning group-invariant distributions with significantly improved data efficiency. In this study, we aim to rigorously quantify this improvement by analyzing the reduction in sample complexity for group-invariant GANs. Our findings indicate that when learning group-invariant distributions, the number of samples required for group-invariant GANs …

abstract adversarial aim arxiv cs.lg data efficiency gans generative generative adversarial networks generators improvement networks replace statistical stat.ml studies study type

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