HCM estimates uncertainty in neural network outputs by quantifying violation of a unit hypersphere constraint on the normalized direction vector.
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Benchmark across architectures and shift regimes finds OOD detector rankings shift with representation collapse; proposes NC-based shortlist predictor and PCA filter without extra OOD data.
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Uncertainty Estimation via Hyperspherical Confidence Mapping
HCM estimates uncertainty in neural network outputs by quantifying violation of a unit hypersphere constraint on the normalized direction vector.
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A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shifts
Benchmark across architectures and shift regimes finds OOD detector rankings shift with representation collapse; proposes NC-based shortlist predictor and PCA filter without extra OOD data.