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

Code

• github.com/mj-will/augmented-flows
  pytorch★ 12

Abstract

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.

Tasks

Benchmark Results

Reproductions