Learning Energy-Based Models by Diffusion Recovery Likelihood

international conference on learning representations, 2020.

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We present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs based on a diffusion process. We achieves high sample quality, stable long-run sampling chains and estimation of likelihood.

Abstract:

While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly ...More

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