A Unifying Theory for Self-Supervised Learning: Latent Distribution Matching
Researchers propose latent distribution matching (LDM) as a new theoretical framework that unifies various self-supervised learning (SSL) methods, including contrastive, non-contrastive, and predictive approaches. The framework also leads to a nonlinear Bayesian filtering model for high-dimensional time series and demonstrates identifiability of latent representations under mild conditions. LDM clarifies the assumptions behind existing SSL methods and offers principled guidance for developing new approaches.
Why it matters: This work provides a unified theoretical foundation for self-supervised learning, potentially enabling more systematic and effective development of representation learning methods.
Full story at: arXiv Statistical ML ↗