Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models [article]

Chin-Wei Huang, Laurent Dinh, Aaron Courville
2020 arXiv   pre-print
In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood. Theoretically, we prove the proposed flow can approximate a Hamiltonian ODE as a universal transport map. Empirically, we demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.
arXiv:2002.07101v1 fatcat:xqhunznulzc23oxiixbkamrx3a