In generalized contrastive learning with imbalanced classes, optimal representations collapse to class means whose angular geometry is determined by class proportions via convex optimization, and extreme imbalance causes all minority classes to collapse to one vector.
arXiv preprint arXiv:2301.00437 , year=
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In multi-label neural collapse, terminal geometry is controlled by the centered label covariance spectrum κ_m derived from label distribution moments, with higher-multiplicity prototypes following class-frequency-weighted synthesis instead of uniform averaging.
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Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets
In generalized contrastive learning with imbalanced classes, optimal representations collapse to class means whose angular geometry is determined by class proportions via convex optimization, and extreme imbalance causes all minority classes to collapse to one vector.
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How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse
In multi-label neural collapse, terminal geometry is controlled by the centered label covariance spectrum κ_m derived from label distribution moments, with higher-multiplicity prototypes following class-frequency-weighted synthesis instead of uniform averaging.