We propose a deep learning framework for modeling complex high-dimensional densities via Nonlinear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing the determinant of the Jacobian and inverse Jacobian is trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood, which is tractable, and unbiased ancestral sampling is also easy. We show that this approach yields good generative models on four image datasets and can be used for inpainting.
REF develop flow-based generative models with affine transformations to make the computation of the Jacobian efficient.
NICE: Non-linear Independent Components Estimation
ICLR 2015
arXiv: Learning
Computer Science Mathematics