View abstract

Session II.4 - Foundations of Data Science and Machine Learning

Saturday, June 17, 15:30 ~ 16:00

Understanding Deep Representation Learning via Neural Collapse

Qing Qu

University of Michigan, United States   -   This email address is being protected from spambots. You need JavaScript enabled to view it.

Recently, an intriguing phenomenon in the final stages of network training has been discovered and caught great interest, in which the last-layer features and classifiers collapse to simple but elegant mathematical structures: all training inputs are mapped to class-specific points in feature space, and the last-layer classifier converges to the dual of the features' class means while attaining the maximum possible margin. This phenomenon, dubbed Neural Collapse, persists across a variety of different network architectures, datasets, and even data domains. Moreover, a progressive neural collapse occurs from shallow to deep layers. This talk leverages the symmetry and geometry of Neural Collapse, and develops a rigorous mathematical theory to explain when and why it happens under the so-called unconstrained feature model. Based upon this, we show how it can be used to provide guidelines to understand and improve transferability with more efficient fine-tuning.

View abstract PDF