A score-based metric computed on corrupted measurements is claimed to equal the KL divergence between training and test image distributions, enabling unsupervised shift detection and adaptation in inverse problems.
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Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models
A score-based metric computed on corrupted measurements is claimed to equal the KL divergence between training and test image distributions, enabling unsupervised shift detection and adaptation in inverse problems.