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📄 Abstract
Abstract: Contrastive learning -- a modern approach to extract useful representations
from unlabeled data by training models to distinguish similar samples from
dissimilar ones -- has driven significant progress in foundation models. In
this work, we develop a new theoretical framework for analyzing data
augmentation-based contrastive learning, with a focus on SimCLR as a
representative example. Our approach is based on the concept of
\emph{approximate sufficient statistics}, which we extend beyond its original
definition in \cite{oko2025statistical} for contrastive language-image
pretraining (CLIP) using KL-divergence. We generalize it to equivalent forms
and general f-divergences, and show that minimizing SimCLR and other
contrastive losses yields encoders that are approximately sufficient.
Furthermore, we demonstrate that these near-sufficient encoders can be
effectively adapted to downstream regression and classification tasks, with
performance depending on their sufficiency and the error induced by data
augmentation in contrastive learning. Concrete examples in linear regression
and topic classification are provided to illustrate the broad applicability of
our results.
Key Contributions
This paper develops a new theoretical framework for data augmentation-based contrastive learning, focusing on SimCLR. It introduces the concept of 'approximate sufficient statistics' to show that contrastive losses yield encoders that are nearly sufficient, and that their performance on downstream tasks depends on this sufficiency.
Business Value
Provides a deeper theoretical understanding of representation learning techniques, which can guide the development of more efficient and effective foundation models for various AI applications.