2026
IdEst: Assessing Self-Supervised Learning Representations via Intrinsic Dimension
ICML 2026poster
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning meaningful representations from unlabeled data. However, the standard protocol for evaluating these representations, linear probing, is computationally expensive, sensitive to hyperparameters, and provides limited insight…