KLIP: Localized distribution shift detection via KL-divergence with diffusion priors in inverse problems
Author(s)
Kheirandish, Alireza
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Abstract
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 as localize OOD patches within an image.
Experimentally, we show that this metric can detect subtle
yet semantically meaningful distribution shifts, such as
the shift from healthy liver CT scans to those with tumors,
and generalizes across different types of diffusion models,
datasets, and inverse problems
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Date
2026-05
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Resource Type
Text
Resource Subtype
Thesis (Masters Degree)