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Main Authors: Desai, Shasvat, Ghose, Debasmita, Chakraborty, Deep
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2502.08134
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author Desai, Shasvat
Ghose, Debasmita
Chakraborty, Deep
author_facet Desai, Shasvat
Ghose, Debasmita
Chakraborty, Deep
contents Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly impacts representation quality, training efficiency, and computational cost. A well-curated set of pairs leads to stronger representations and faster convergence. As contrastive pre-training sees wider adoption for solving downstream tasks, data curation becomes essential for optimizing its effectiveness. In this survey, we attempt to create a taxonomy of existing techniques for positive and negative pair curation in contrastive learning, and describe them in detail.
format Preprint
id arxiv_https___arxiv_org_abs_2502_08134
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters
Desai, Shasvat
Ghose, Debasmita
Chakraborty, Deep
Computer Vision and Pattern Recognition
Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly impacts representation quality, training efficiency, and computational cost. A well-curated set of pairs leads to stronger representations and faster convergence. As contrastive pre-training sees wider adoption for solving downstream tasks, data curation becomes essential for optimizing its effectiveness. In this survey, we attempt to create a taxonomy of existing techniques for positive and negative pair curation in contrastive learning, and describe them in detail.
title A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2502.08134