Learned Generative Priors Can Inherit Undetectable Overconfidence from Legacy Reconstructions
A new arXiv preprint highlights that learned generative priors for Bayesian inverse problems, when trained on legacy reconstructions instead of ground-truth data, can produce overconfident uncertainty estimates that are undetectable during deployment. The authors demonstrate that this 'prior laundering' leads to inherited overconfidence in measurement directions not resolved by the data, and that standard validation methods may fail to detect this issue. They recommend explicitly reporting which measurement directions are resolved by the data to distinguish between justified and inherited confidence.
Why it matters: This finding raises concerns about the reliability of uncertainty estimates in fields like medical and seismic imaging, where ground-truth data are scarce and legacy reconstructions are commonly used for training.
Full story at: arXiv Statistical ML ↗