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

Understand

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.