研究人员开发了一个新的理论框架，用于理解流模型逆问题求解器，这些求解器用于解决成像逆问题。这种新方法，称为后验传输（posterior-transport），揭示了这些求解器中的条件化是通过重加权源分布而非漂移校正来实现的。该分析提出了一种更有效且有原则的速度校正求解器，该求解器在各种先验和分布外设置中表现出竞争力，同时还能产生具有准确不确定性量化的多样化后验样本。 AI
影响 这项研究提供了对流模型求解器更深入的理论理解，并引入了一种更有效的采样方法，有可能改善逆问题解决方案中的不确定性量化。
排序理由 该集群包含两篇arXiv论文，详细介绍了使用流模型解决逆问题的新理论框架和方法。
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arXiv:2606.26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs). The method addresses three challenges in PDE-…
We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs). The method addresses three challenges in PDE-constrained inversion: implicit sample-based prior…
arXiv:2606.24516v1 Announce Type: new Abstract: A growing family of training-free solvers -- FlowDPS, FLOWER, PnP-Flow and their diffusion ancestors (DPS, DAPS) -- repurpose a pretrained flow-matching prior to solve imaging inverse problems by adding a measurement-guidance term t…
A growing family of training-free solvers -- FlowDPS, FLOWER, PnP-Flow and their diffusion ancestors (DPS, DAPS) -- repurpose a pretrained flow-matching prior to solve imaging inverse problems by adding a measurement-guidance term to the deterministic probability-flow ODE. Despit…
Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow Annealing Posterior Sampling (FAPS), to our knowledge the first function-space posterior sampling framework that unifies stochastic-…