Wasserstein

a gan trains a generator by making it play a game against a learned critic: the generator \(G\) maps noise to samples, the discriminator \(D\) tries to tell those samples from real data, and each improves by exploiting the other’s current weakness — density estimation recast as a two-player minimax game (goodfellow et al. 2014, generative adversarial networks). 𐃏 the framework is treated in ch. 20 of (Goodfellow, Ian, 2016).