Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis

By H. Waida et al

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Table of Contents

1 Introduction
1.1 Contributions
1.2 Related Work
2 Preliminaries
2.1 Problem Setup
2.2 Reproducing Kernels
3 Kernel Contrastive Learning

Summary

Contrastive learning is an efficient approach to self-supervised representation learning. Recent studies have focused on the theoretical understanding of contrastive learning to enhance self-supervised learning algorithms. This paper delves into the mechanism of contrastive learning by analyzing the clusters of feature vectors output from an encoder model pretrained by contrastive learning. Theoretical perspectives and formulations of similarity between data are introduced to shed light on this fundamental question. The paper presents a new upper bound for the classification error of downstream tasks and establishes a generalization error bound for the Kernel Contrastive Learning (KCL) framework. Theoretical results indicate the generalization ability of KCL to downstream classification tasks. The study utilizes a kernel-based contrastive learning framework termed KCL to provide a unified theoretical perspective on contrastive learning. Several contributions are made to elucidate the mechanism of contrastive learning and to extend the theoretical understanding of self-supervised representation learning.