Density Estimation using Real NVP
Venue
arXiv preprint (2016)
Publication Year
2016
Authors
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
BibTeX
Abstract
Unsupervised learning of probabilistic models is a central yet challenging problem
in machine learning. Specifically, designing models with tractable learning,
sampling, inference and evaluation is crucial in solving this task. We extend the
space of such models using real-valued non-volume preserving (real NVP)
transformations, a set of powerful invertible and learnable transformations,
resulting in an unsupervised learning algorithm with exact log-likelihood
computation, exact sampling, exact inference of latent variables, and an
interpretable latent space. We demonstrate its ability to model natural images on
four datasets through sampling, log-likelihood evaluation and latent variable
manipulations.
