Flow Contrastive Estimation of Energy-Based Models
Abstract
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.
Cite
@article{arxiv.1912.00589,
title = {Flow Contrastive Estimation of Energy-Based Models},
author = {Ruiqi Gao and Erik Nijkamp and Diederik P. Kingma and Zhen Xu and Andrew M. Dai and Ying Nian Wu},
journal= {arXiv preprint arXiv:1912.00589},
year = {2020}
}