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Learning Distributions by Generative Adversarial Networks: Approximation and Generalization. (arXiv:2205.12601v1 [cs.LG])
May 26, 2022, 1:11 a.m. | Yunfei Yang
stat.ML updates on arXiv.org arxiv.org
We study how well generative adversarial networks (GAN) learn probability
distributions from finite samples by analyzing the convergence rates of these
models. Our analysis is based on a new oracle inequality that decomposes the
estimation error of GAN into the discriminator and generator approximation
errors, generalization error and optimization error. To estimate the
discriminator approximation error, we establish error bounds on approximating
H\"older functions by ReLU neural networks, with explicit upper bounds on the
Lipschitz constant of the network or …
approximation arxiv generative adversarial networks learning networks
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