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.
- Pub Date:
- May 2016
- Computer Science - Machine Learning;
- Computer Science - Artificial Intelligence;
- Computer Science - Neural and Evolutionary Computing;
- Statistics - Machine Learning
- 10 pages of main content, 3 pages of bibliography, 18 pages of appendix. Accepted at ICLR 2017