KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
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. We propose an OOD detection metric based on the Kullback-Leibler divergence between the diffusion prior and the posterior distribution, that (i) does not require any calibration data or knowledge of the shifted distribution, and (ii) can detect whole images as OOD as well...
| Authors | Alireza Kheirandish, Jihoon Hong, Sara Fridovich-Keil |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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