用解耦扩散模型提升贝叶斯反问题求解精度与效率
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
- 基于解耦扩散设计序列蒙特卡洛采样，支持更大样本更新步长
- 算法在合成数据、蛋白质及图像上均实现精确求解
- 可扩展至离散数据，适用于需高保真生成的逆问题
近期研究利用预训练生成扩散模型作为贝叶斯反问题的先验。本文提出一种针对线性高斯反问题的序列蒙特卡洛方法，基于“解耦扩散”设计，使样本更新幅度更大。该方法渐近精确，在合成数据、蛋白质和图像数据上均验证了有效性。此外，还展示了该方法可扩展至离散数据。
原文摘要 · Abstract (English)
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designing a sequential Monte Carlo method for linear-Gaussian inverse problems which builds on "decoupled diffusion", where the generative process is designed such that larger updates to the sample are possible. The method is asymptotically exact and we demonstrate the effectiveness of our Decoupled Diffusion Sequential Monte Carlo (DDSMC) algorithm on both synthetic as well as protein and image data. Further, we demonstrate how the approach can be extended to discrete data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。