IO-CUE trains a small auxiliary network on (input, frozen output) pairs with a detached Gaussian NLL objective to estimate prediction variance post-hoc, with augmented probe data improving OOD detection.
Pitfalls of epistemic uncertainty quantification through loss minimisation
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Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks
IO-CUE trains a small auxiliary network on (input, frozen output) pairs with a detached Gaussian NLL objective to estimate prediction variance post-hoc, with augmented probe data improving OOD detection.