Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. (arXiv:2205.11508v2 [cs.LG] UPDATED)

Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has
recently reached a milestone: outperforming supervised methods in many
modalities\dots the theoretical foundations are limited, method-specific, and
fail to provide principled design guidelines to practitioners. In this paper,
we propose a unifying framework under the helm of spectral manifold learning to
address those limitations. Through the course of this study, we will rigorously
demonstrate that VICReg, SimCLR, BarlowTwins et al. correspond …

arxiv embedding global learning self-supervised learning supervised learning