UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
Keywords: self-supervised learning, imaging inverse problems
TL;DR: We present an extension of SURE that doesn't require knowledge of the noise level, and a new mathematical framework to understand self-supervised learning losses.
Abstract: Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Most existing methods cluster around two classes: i) Stein's Unbiased Risk Estimate (SURE) and similar approaches that assume full knowledge of the noise distribution, and ii) Noise2Self and similar cross-validation methods that require very mild knowledge about the noise distribution. The first class of methods tends to be impractical, as the noise level is often unknown in real-world applications, and the second class is often suboptimal compared to supervised learning.
In this paper, we provide a theoretical framework that characterizes this expressivity-robustness trade-off and propose a new approach based on SURE, but unlike the standard SURE, does not require knowledge about the noise level. Throughout a series of experiments, we show that the proposed estimator outperforms other existing self-supervised methods on various imaging inverse problems.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 7610
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