Vacuity-based OOD detection in evidential deep learning is highly sensitive to class cardinality differences between ID and OOD, which can artificially inflate AUROC and AUPR without any change in model predictions.
Calibrating LLMs with Information-Theoretic Evidential Deep Learning, February 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.
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Rethinking Vacuity for OOD Detection in Evidential Deep Learning
Vacuity-based OOD detection in evidential deep learning is highly sensitive to class cardinality differences between ID and OOD, which can artificially inflate AUROC and AUPR without any change in model predictions.
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Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation
ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.