GEODE uses per-sample cosine-similarity scaling in a norm loss to preserve feature geometry for universal scorer-compatible OOD detection, matching or exceeding OE performance on CIFAR benchmarks.
International Conference on Machine Learning , year=
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Hyperspherical Pooled Mahalanobis (HPM) normalizes frozen long-tailed features to the unit sphere and uses pooled ridge-regularized covariance to improve raw Mahalanobis OOD scoring, lifting AUROC from 46.49 to 85.67 on CIFAR-10-LT.
citing papers explorer
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GEODE: Angle-Adaptive OOD Detection with Universal Scorer Compatibility
GEODE uses per-sample cosine-similarity scaling in a norm loss to preserve feature geometry for universal scorer-compatible OOD detection, matching or exceeding OE performance on CIFAR benchmarks.
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Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry
Hyperspherical Pooled Mahalanobis (HPM) normalizes frozen long-tailed features to the unit sphere and uses pooled ridge-regularized covariance to improve raw Mahalanobis OOD scoring, lifting AUROC from 46.49 to 85.67 on CIFAR-10-LT.