Wasserstein GAN

The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it mean to learn a probability distribution? The classical answer to this is to learn a probability density. This is often done by defining a parametric family of densities (Pθ)θ∈Rd and finding the one that maximized the likelihood on our data: if we have real data examples {x}i=1, we would solve the problem

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