arXiv stat.ML
· Papers
Understanding Self-Supervised Learning via Latent Distribution Matching
arXiv:2605.03517v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework that explains the diverse existing methods and guides the design of new ones. We cast SSL as latent distribution