Web: http://arxiv.org/abs/2209.06853

Sept. 16, 2022, 1:13 a.m. | Xinwei Shen, Kani Chen, Tong Zhang

stat.ML updates on arXiv.org arxiv.org

Generative Adversarial Networks (GANs) have achieved great success in data
generation. However, its statistical properties are not fully understood. In
this paper, we consider the statistical behavior of the general $f$-divergence
formulation of GAN, which includes the Kullback--Leibler divergence that is
closely related to the maximum likelihood principle. We show that for
parametric generative models that are correctly specified, all $f$-divergence
GANs with the same discriminator classes are asymptotically equivalent under
suitable regularity conditions. Moreover, with an appropriately chosen local …

analysis arxiv divergence gan math statistical

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