Generative Adversarial Nets - NIPS
value function V(G;D): min G max D V(D;G) = E x˘p data(x)[logD(x)]+E z˘p z(z)[log(1 D(G(z)))]: (1) In the next section, we present a theoretical analysis of adversarial nets, essentially showing that the training criterion allows one to recover the data generating distribution as Gand Dare given enough capacity, i.e., in the non-parametric limit.
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