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arXiv · 2410.05837

A noise-corrected Langevin algorithm and sampling by half-denoising

Aapo Hyvärinen
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Abstract

The Langevin algorithm is a classic method for sampling from a given pdf in a real space. In its basic version, it only requires knowledge of the gradient of the log-density, also called the score function. However, in deep learning, it is often easier to learn the so-called "noisy-data score function", i.e. the gradient of the log-density of noisy data, more precisely when Gaussian noise is added to the data. Such an estimate is biased and complicates the use of the Langevin method. Here, we propose a noise-corrected version of the Langevin algorithm, where the bias due to noisy data is removed, at least regarding first-order terms. Unlike diffusion models, our algorithm only needs to know the noisy score function for one single noise level. We further propose a simple special case which has an interesting intuitive interpretation of iteratively adding noise the data and then attempting to remove half of that noise.

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Aapo Hyvärinen. 2024-10-08. A noise-corrected Langevin algorithm and sampling by half-denoising. https://arxiv.org/abs/2410.05837

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