Neural Collapse by Design: Learning Class Prototypes on the Hypersphere

In three linesTwo supervised classification paradigms (cross-entropy and contrastive learning) converge to Neural Collapse, a theoretical optimum. Authors propose NTCE and NONL, two normalized losses reaching NC in <7.5% of CE iterations, with +5.5% transfer learning improvement and +8.7% under class imbalance on ImageNet-1K.

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