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arXiv · Statistics Machine Learning· Dai Hai Nguyen, Duc Dung Nguyen·· 4 hr agoAI score27

RED-KLwSGS:扩散先验下成像逆问题的加速后验采样

Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors

AI brief

研究者提出 RED-KLwSGS 采样方法,在 Split Gibbs 采样框架中保留数据变量的精确高斯更新,并用单次去噪得分驱动的欠阻尼(动能)Langevin 扩散更新辅助变量,单次迭代成本与 Langevin-within-SGS 相当。

Source: arXiv · Statistics Machine Learning · arxiv.org