Neural Collapse: A Review on Modelling Principles and Generalization
This review synthesizes existing research on a fundamental behavior in neural networks, which is incremental as it consolidates prior findings without introducing new methods or results.
The paper reviews the Neural Collapse phenomenon in deep neural networks, where during terminal training phases, within-class variability becomes minimal and class means form a simplex equiangular tight frame, simplifying decision rules to nearest-class center, and discusses its implications for generalization and transfer learning.
Deep classifier neural networks enter the terminal phase of training (TPT) when training error reaches zero and tend to exhibit intriguing Neural Collapse (NC) properties. Neural collapse essentially represents a state at which the within-class variability of final hidden layer outputs is infinitesimally small and their class means form a simplex equiangular tight frame. This simplifies the last layer behaviour to that of a nearest-class center decision rule. Despite the simplicity of this state, the dynamics and implications of reaching it are yet to be fully understood. In this work, we review the principles which aid in modelling neural collapse, followed by the implications of this state on generalization and transfer learning capabilities of neural networks. Finally, we conclude by discussing potential avenues and directions for future research.