A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn di...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 46; no. 12; pp. 9052 - 9071
Main Authors: Gui, Jie, Chen, Tuo, Zhang, Jing, Cao, Qiong, Sun, Zhenan, Luo, Hao, Tao, Dacheng
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2024
Subjects:
contrastive learning
generative model
Image recognition
Machine learning algorithms
representation learning
Reviews
Self-supervised learning
Supervised learning
Task analysis
Training
transfer learning
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. First, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Second, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2024.3415112