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

• URL: http://arxiv.org/abs/2002.07101v1
• Date: Mon, 17 Feb 2020 17:45:48 GMT
• Title: Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
• Authors: Chin-Wei Huang, Laurent Dinh, Aaron Courville
• Abstract summary: 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. We demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.
• Score: 11.206144910991481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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