KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems

ArXi:2605.31596v1 Announce Type: cross Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, existing approaches to OOD detection often require some knowledge of the shifted distribution, fail to detect subtle or localized distribution shifts, and operate on full images, rather than the indirect measurements available in inverse problems.