English(EN) Learning Clean Generative Processes from Noisy Measurements

新方法利用一致性关系从噪声数据中学习干净的图像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种从噪声观测中学习干净图像生成模型的方法，特别适用于扩散过程和非高斯噪声。该技术利用Noise2Noise回归从独立的噪声视图中学习预测，但需要一致性才能生成低于训练数据噪声水平的图像。这种一致性关系确保了预测在继续采样过程后等于其期望值，这一点已通过泊松分布和二项分布等各种分布得到验证。在CIFAR-10、显微镜和合成孔径雷达数据上的实验证明了该方法仅从噪声视图学习后验预测并在超出数据噪声水平的延续方面进行修复的有效性。 AI

影响这项研究可以提高生成模型在噪声环境下的性能，可能影响医学成像和遥感等领域。

排序理由这是一篇详细介绍生成模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Fabian Kruse, Lorenzo Baldassari, Ivan Dokmani\'c · 2026-11-02 05:00

Learning Clean Generative Processes from Noisy Measurements

arXiv:2610.42036v1 Announce Type: cross Abstract: We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require posterior predictions at every noise level. We first show that it …

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Learning Clean Generative Processes from Noisy Measurements

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