A Two-Step Computation of the Exact GAN Wasserstein Distance

ICML 2018 Huidong LiuXianfeng GUDimitris Samaras

In this paper, we propose a two-step method to compute the Wasserstein distance in Wasserstein Generative Adversarial Networks (WGANs): 1) The convex part of our objective can be solved by linear programming; 2) The non-convex residual can be approximated by a deep neural network. We theoretically prove that the proposed formulation is equivalent to the discrete Monge-Kantorovich dual formulation... (read more)

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