Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets
Conventional semi-supervised learning (SSL) encounters challenges in effectively addressing issues associated with long-tail datasets, primarily stemming from imbalances within a dataset. Existing contrastive SSL methods typically rely on pseudo-labels generated from unlabeled data, which can be ina...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11039800/ |
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| author | Dongyoung Kim Won-Sook Lee Young-Woong Ko Jeong-Gun Lee |
| author_facet | Dongyoung Kim Won-Sook Lee Young-Woong Ko Jeong-Gun Lee |
| author_sort | Dongyoung Kim |
| collection | DOAJ |
| description | Conventional semi-supervised learning (SSL) encounters challenges in effectively addressing issues associated with long-tail datasets, primarily stemming from imbalances within a dataset. Existing contrastive SSL methods typically rely on pseudo-labels generated from unlabeled data, which can be inaccurate and introduce confirmation bias toward majority classes. To address this issue, we propose Separated and Independent Contrastive Semi-Supervised Learning (SICSSL), which applies supervised contrastive learning separately and independently to labeled and unlabeled samples. We validate SICSSL on four public benchmark datasets: CIFAR-10-LT, CIFAR-100-LT, STL-10-LT, and ImageNet-127. The results demonstrate consistent performance improvements over existing contrastive learning approaches. Furthermore, SICSSL is modular and easily integrates with recent SSL frameworks, including Auxiliary Balanced Classifier (ABC) and Adaptive Consistency Regularizer (ACR), enabling improved robustness in imbalanced semi-supervised settings. Source code is available at <uri>https://github.com/dongyyyyy/SICSSL</uri> |
| format | Article |
| id | doaj-art-33eccef4738b4f6798a72187916439b7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-33eccef4738b4f6798a72187916439b72025-08-20T03:24:06ZengIEEEIEEE Access2169-35362025-01-011310571210572310.1109/ACCESS.2025.358073811039800Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced DatasetsDongyoung Kim0https://orcid.org/0000-0003-1998-7784Won-Sook Lee1Young-Woong Ko2https://orcid.org/0000-0003-3920-9314Jeong-Gun Lee3https://orcid.org/0000-0001-6218-4560Department of Computer Engineering, Hallym University, Chuncheon, South KoreaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaDepartment of Computer Engineering, Hallym University, Chuncheon, South KoreaDepartment of Computer Engineering, Hallym University, Chuncheon, South KoreaConventional semi-supervised learning (SSL) encounters challenges in effectively addressing issues associated with long-tail datasets, primarily stemming from imbalances within a dataset. Existing contrastive SSL methods typically rely on pseudo-labels generated from unlabeled data, which can be inaccurate and introduce confirmation bias toward majority classes. To address this issue, we propose Separated and Independent Contrastive Semi-Supervised Learning (SICSSL), which applies supervised contrastive learning separately and independently to labeled and unlabeled samples. We validate SICSSL on four public benchmark datasets: CIFAR-10-LT, CIFAR-100-LT, STL-10-LT, and ImageNet-127. The results demonstrate consistent performance improvements over existing contrastive learning approaches. Furthermore, SICSSL is modular and easily integrates with recent SSL frameworks, including Auxiliary Balanced Classifier (ABC) and Adaptive Consistency Regularizer (ACR), enabling improved robustness in imbalanced semi-supervised settings. Source code is available at <uri>https://github.com/dongyyyyy/SICSSL</uri>https://ieeexplore.ieee.org/document/11039800/Semi-supervised learningimbalanced semi-supervised learninglong-tail problemcontrastive learning |
| spellingShingle | Dongyoung Kim Won-Sook Lee Young-Woong Ko Jeong-Gun Lee Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets IEEE Access Semi-supervised learning imbalanced semi-supervised learning long-tail problem contrastive learning |
| title | Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets |
| title_full | Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets |
| title_fullStr | Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets |
| title_full_unstemmed | Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets |
| title_short | Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets |
| title_sort | separated and independent contrastive semi supervised learning for imbalanced datasets |
| topic | Semi-supervised learning imbalanced semi-supervised learning long-tail problem contrastive learning |
| url | https://ieeexplore.ieee.org/document/11039800/ |
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