The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning
arXiv:2606.04280v1 Announce Type: new Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood. We develop a measure-theoretic framework formalizing the diversity condition, a support requirement on positive-pair sampling that is necessary for isometric latent recovery. We show that the standard full-support von Mises-Fisher setting implies the satisfaction of the diversity condition and as a consequence global contrastive loss minimizers recover
This research provides a foundational understanding of contrastive learning just as it becomes a dominant paradigm for self-supervised AI, addressing fundamental theoretical gaps in its effectiveness.
A strategic reader should care because deeper theoretical understanding of core AI mechanisms like contrastive learning enables more robust, efficient, and reliable AI development, impacting future capabilities across various applications.
The theoretical framework clarifies the necessary conditions for effective contrastive representation learning, shifting AI development towards more principled design choices and away from purely empirical exploration.
- · AI researchers
- · Machine learning framework developers
- · Enterprises deploying self-supervised models
- · AI developers relying on trial-and-error
- · Less theoretically grounded AI research
Improved design principles for self-supervised learning algorithms lead to more effective AI models.
Faster innovation cycles in AI due to a better understanding of fundamental learning mechanisms.
Enhanced AI capabilities across critical sectors, potentially accelerating progress in autonomous agents and complex decision-making systems.
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Read at arXiv cs.LG