Sample-wise neural collapse reveals that feature-classifier misalignment drives TTA degradation under shifts, which NCTTA corrects via hybrid geometric-predictive targets.
Explor- ing deep neural networks via layer-peeled model: Minority collapse in imbalanced training.Proceedings of the National Academy of Sciences, 118(43):e2103091118, 2021
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Neural Collapse in Test-Time Adaptation
Sample-wise neural collapse reveals that feature-classifier misalignment drives TTA degradation under shifts, which NCTTA corrects via hybrid geometric-predictive targets.