ASCL: Accelerating semi‐supervised learning via contrastive learning Article Swipe
Haixiong Liu
,
Zuoyong Li
,
Jiawei Wu
,
Kun Zeng
,
Rong Hu
,
Wei Zeng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.8293
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.8293
Summary SSL (semi‐supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo‐labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo‐labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug‐and‐play method named Accelerating semi‐supervised learning via contrastive learning (ASCL). This method combines contrastive learning with uncertainty‐based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo‐labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi‐supervised learning. Uncertainty‐based selection provides a robust mechanism for acquiring pseudo‐labels. The combination of the contrastive learning module and the uncertainty‐based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state‐of‐the‐art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR‐10 dataset, the application of ASCL to Pseudo‐label, UDA (unsupervised data augmentation for consistency training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cpe.8293
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.8293
- OA Status
- bronze
- Cited By
- 1
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403219640
All OpenAlex metadata
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https://openalex.org/W4403219640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/cpe.8293Digital Object Identifier
- Title
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ASCL: Accelerating semi‐supervised learning via contrastive learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-08Full publication date if available
- Authors
-
Haixiong Liu, Zuoyong Li, Jiawei Wu, Kun Zeng, Rong Hu, Wei ZengList of authors in order
- Landing page
-
https://doi.org/10.1002/cpe.8293Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.8293Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cpe.8293Direct OA link when available
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Computer science, Artificial intelligence, Machine learning, Consistency (knowledge bases), Mutual information, Class (philosophy), Semi-supervised learning, Selection (genetic algorithm), Convergence (economics), Active learning (machine learning), Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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