Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows

• URL: http://arxiv.org/abs/2206.06672v2
• Date: Thu, 22 Jun 2023 23:34:39 GMT
• Title: Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
• Authors: Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
• Abstract summary: This paper studies the likelihood-free training of flows and proposes the energy objective. The energy objective is determinant-free and supports flexible model architectures. Our findings question the use of maximum likelihood as an objective or a metric, and contribute to a scientific study of its role in generative modeling.
• Score: 5.096977916317878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objective is determinant-free and supports flexible model architectures that are not easily compatible with maximum likelihood training, including semi-autoregressive energy flows, a novel model family that interpolates between fully autoregressive and non-autoregressive models. Energy flows feature competitive sample quality, posterior inference, and generation speed relative to likelihood-based flows; this performance is decorrelated from the quality of log-likelihood estimates, which are generally very poor. Our findings question the use of maximum likelihood as an objective or a metric, and contribute to a scientific study of its role in generative modeling.

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