Divergence-Based Similarity Function for Multi-View Contrastive Learning — Paper

Abstract

-cross Abstract: Recent success in contrastive learning has sparked growing interest in more effectively leveraging multiple augmented views of data. While prior methods incorporate multiple views at the loss or feature level, they primarily capture pairwise relationships and fail to model the joint structure across all views. In this work, we propose a divergence-based similarity function (DSF) that explicitly captures the joint structure by representing each set of augmented views as a distribution and measuring similarity as the divergence between distributions. Extensive experiments demonstrate that DSF consistently improves performance across diverse tasks, including kNN classification, linear evaluation, transfer learning, and distribution shift, while also achieving greater efficiency than other multi-view methods. Furthermore, we establish a connection between DSF and cosine similarity, and demonstrate that, unlike cosine similarity, DSF operates effectively without the need for tuning a temperature hyperparameter.

Authors 3

Jaehyoung Jeon, Cheolsu Lim, Myungjoo Kang

Specification

Official page: arxiv.org/abs/2507.06560
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2507.06560
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.CV, cs.LG
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
DOI: 10.1007/978-981-92-1462-4_5
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2507.06560
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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10

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T110

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2 h ago

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None

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Authors: Jaehyoung Jeon, Cheolsu Lim, Myungjoo Kang

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2507.06560	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDivergence-Based Similarity Function for Multi-View Contrastive Learning
New paper: Divergence-Based Similarity Function for Multi-View Contrastive Learning
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	38 min ago	1

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