A Wasserstein Minimum Velocity Approach to Learning Unnormalized ModelsDownload PDF

16 Oct 2019 (modified: 06 Dec 2019)AABI 2019 Symposium Blind SubmissionReaders: Everyone
  • Keywords: energy-based model, optimal transport, variational auto-encoder, Wasserstein auto-encoder, score matching
  • TL;DR: We present a scalable approximation to a wide range of EBM objectives, and applications in implicit VAEs and WAEs
  • Abstract: Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this paper,we connect a general family of learning objectives including score matching to Wassersteingradient flows. This connection enables us to design a scalable approximation to theseobjectives, with a form similar to single-step contrastive divergence. We present applications in training implicit variational and Wasserstein auto-encoders with manifold-valued priors.
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