1 University of California, Los Angeles (UCLA)
2 Google Brain
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.
The paper can be downloaded here.
The TensorFlow2 code is coming soon!
If you wish to use the code or results, please cite the following paper:
The work is partially supported by DARPA XAI project N66001-17-2-4029 and ARO project W911NF1810296. We thank Pavel Sountsov, Alex Alemi, Matthew D. Hoffman and Srinivas Vasudevan for their helpful discussions.