Density estimation using Real NVP

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Authors Samy Bengio, Jascha Sohl-Dickstein, Laurent Dinh
Journal/Conference Name 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Multiple
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