Neural Collapse by Design: Learning Class Prototypes on the Hypersphere
Researchers have introduced new methods, NTCE and NONL, to improve supervised classification by achieving Neural Collapse (NC) more efficiently. These techniques address limitations in existing paradigms like cross-entropy and supervised contrastive learning. By treating supervised learning as prototype learning on a hypersphere, the new losses enable faster convergence to NC and yield significant improvements in transfer learning and robustness, especially under class imbalance. AI
IMPACT Introduces novel losses that accelerate convergence to optimal classification geometry and improve model robustness.