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A Convex Duality Framework for GANs
[article]
2018
arXiv
pre-print
Generative adversarial network (GAN) is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples. Given an unconstrained discriminator able to approximate any function, this game reduces to finding the generative model minimizing a divergence measure, e.g. the Jensen-Shannon (JS) divergence, to the data distribution. However, in practice the discriminator is constrained to be in a smaller
arXiv:1810.11740v1
fatcat:uqurcy24lbfgthquds6fyjm24u