Sinkhorn AutoEncoders

ICLR 2019 Giorgio PatriniRianne van den BergPatrick ForréMarcello CarioniSamarth BhargavMax WellingTim GeneweinFrank Nielsen

Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error... (read more)

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