ZHANG J R, CAO Z X, WANG T,et al.Data augmentation-based contrastive self-supervised learning： A review[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):48-59.

doi:10.16186/j.cnki.1673-9787.2026010020

Received:2026/01/13

Revised:2026/05/23

Published:2026-07-24

Data augmentation-based contrastive self-supervised learning： A review

Zhang Jiarui1, Cao Zhexiao1, Wang Tian1, Wang Chuanyun2, Teng Jing3, Fu Yao4

1.School of Artificial Intelligence， Beihang University， Beijing  100191， China；2.School of Artificial Intelligence， Shenyang Aerospace University， Shenyang  110121， Liaoning， China；3.School of Control and Computer Engineering， North China Electric Power University， Beijing  102206， China；4.Changchun Institute of Optics， Fine Mechanics and Physics， Chinese Academy of Sciences， Changchun  130033， Jilin， China

Abstract: Significance Traditional supervised learning relies heavily on large-scale manually annotated datasets， which incur high labeling costs and introduce potential human bias， thereby limiting model generalization. Meanwhile， the vast amount of unlabeled data remains underutilized. As a result， self-supervised learning has emerged as a major research focus. Among its paradigms， contrastive learning has become a representative discriminative approach. It constructs positive and negative sample pairs and learns representations by pulling similar instances closer while pushing dissimilar ones apart in the feature space， enabling models to capture high-level semantic features in a self-supervised manner. Compared with generative methods， contrastive learning demonstrates stronger transferability and lower computational costs， providing a promising direction for universal representation learning. Progress  This paper systematically reviews the theoretical framework and core components of contrastive learning， including data augmentation strategies that enforce semantic invariance， sample construction mechanisms that determine feature discriminability， Siamese-network-based encoder architectures， and loss functions such as InfoNCE. Representative methods， including MoCo， SimCLR， Barlow Twins， and SimSiam， are further summarized in terms of their technical designs. In addition， emerging applications are discussed such as representation learning under long-tailed distributions and multimodal vision-language alignment. key challenges are analyzed in contrastive learning， including false negative sample issues in large-batch training and modality heterogeneity in cross-modal scenarios.  Conclusions and Prospects Contrastive learning effectively alleviates the reliance on large-scale labeled data， providing a solid foundation for developing perception systems in artificial general intelligence. Further research should focus on designing lightweight and efficient sample matching mechanisms to reduce computational complexity， while also improving theoretical interpretability to better understand feature disentanglement and generalization mechanisms.

Key words:contrastive learning;self-supervised learning;data augmentation;long-tailed distribution;multimodal learning