A self-supervised conformal prediction method with equivariant bootstrapping enables uncertainty quantification for ill-posed imaging inverse problems such as weak lensing mass mapping without requiring ground truth calibration data.
Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems: 27th International Conference on Artificial Intelligence and Statistics 2024,
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Self-Supervised Conformal Prediction with Equivariant Bootstrapping for Image Uncertainty Quantification
A self-supervised conformal prediction method with equivariant bootstrapping enables uncertainty quantification for ill-posed imaging inverse problems such as weak lensing mass mapping without requiring ground truth calibration data.