arXiv:2605.03517v3 Announce Type: replace 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 matching (LDM): learning representations that maximize their log-probability under an assumed latent model (alignment), while maximizing latent entropy to prevent collapse (uniformity). This view unifies independent component analysis with contrastive, non-contrastive, and predictive
The proliferation of various self-supervised learning methods necessitates a unifying theoretical framework to guide future research and development, moving beyond empirical successes.
A unifying theoretical framework for self-supervised learning can accelerate AI development by enabling more systematic design of robust and efficient models, reducing trial-and-error.
The understanding of self-supervised learning shifts from a collection of disparate techniques to a cohesive theoretical foundation based on latent distribution matching.
- · AI researchers
- · Deep learning practitioners
- · AI-reliant industries
- · Unprincipled heuristic-based model development
Increased efficiency and effectiveness in training large AI models with less labeled data.
Faster development and deployment of more general-purpose AI systems across various domains.
Potential for a new generation of AI models that can learn from vast amounts of unlabeled data with minimal human supervision, accelerating progress towards AGI.
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Read at arXiv cs.LG