GenCE is a strictly proper loss obtained by normalizing each sample's softmax against the batch predictions, outperforming cross-entropy in low-data and imbalanced regimes with better calibration and OOD detection.
The alzheimer’s disease neu- roimaging initiative 2 pet core: 2015.Alzheimer’s & Dementia, 11(7):757–771
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Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classification
GenCE is a strictly proper loss obtained by normalizing each sample's softmax against the batch predictions, outperforming cross-entropy in low-data and imbalanced regimes with better calibration and OOD detection.