KLIP

KLIP: Localized OOD Detection in Inverse Problems via KL-Divergence with Diffusion Priors

KLIP proposes an out-of-distribution detection metric for computational imaging that computes KL-divergence between a diffusion model prior and the posterior distribution. Unlike prior approaches, it requires no calibration data or knowledge of the shifted distribution, and can both flag whole images and localize OOD patches within images. The method is validated on medical imaging tasks such as detecting liver tumors in CT scans and generalizes across diffusion model architectures, datasets, and inverse problem types.