PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization
As artificial intelligence developed rapidly, deep learning models have been applied in various domains. While labeling is crucial to training models in fields that demand specific knowledge, producing such labeled datasets is expensive. Semi-supervised learning (SSL) is becoming a potential solutio...
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| Format: | Article |
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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/10918676/ |
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| author | Mikyung Kang Sooyon Seo Moohong Min |
| author_facet | Mikyung Kang Sooyon Seo Moohong Min |
| author_sort | Mikyung Kang |
| collection | DOAJ |
| description | As artificial intelligence developed rapidly, deep learning models have been applied in various domains. While labeling is crucial to training models in fields that demand specific knowledge, producing such labeled datasets is expensive. Semi-supervised learning (SSL) is becoming a potential solution to this problem, utilizing data without label information. However, conventional SSL methods focus solely on predicting data classes while minimizing classification loss, which can lead to ambiguous decision boundaries, especially for classes with similar characteristics. In this study, we propose enhancing the baseline SSL method by incorporating a contrastive loss. We emphasize that samples near the decision boundaries have a negative impact on the model’s performance. Our method addresses the issue of uncertain boundaries in the representation space by focusing on the unique characteristics of each class. We conducted experiments to validate the effectiveness of our proposed method using a limited number of labeled samples. The results demonstrate that our method effectively enhances performance, particularly in environments with limited labeled data, as evidenced by visual analysis. |
| format | Article |
| id | doaj-art-2ff26cb699494e5dbb4a4a1871f868a7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2ff26cb699494e5dbb4a4a1871f868a72025-08-20T02:55:53ZengIEEEIEEE Access2169-35362025-01-0113445434455410.1109/ACCESS.2025.354946510918676PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency RegularizationMikyung Kang0https://orcid.org/0000-0002-4412-3382Sooyon Seo1https://orcid.org/0009-0002-5595-6627Moohong Min2https://orcid.org/0000-0001-8979-1344Department of Applied Data Science, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Immersive Media Engineering, Sungkyunkwan University, Seoul, Republic of KoreaDepartment of Computer Education, Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Republic of KoreaAs artificial intelligence developed rapidly, deep learning models have been applied in various domains. While labeling is crucial to training models in fields that demand specific knowledge, producing such labeled datasets is expensive. Semi-supervised learning (SSL) is becoming a potential solution to this problem, utilizing data without label information. However, conventional SSL methods focus solely on predicting data classes while minimizing classification loss, which can lead to ambiguous decision boundaries, especially for classes with similar characteristics. In this study, we propose enhancing the baseline SSL method by incorporating a contrastive loss. We emphasize that samples near the decision boundaries have a negative impact on the model’s performance. Our method addresses the issue of uncertain boundaries in the representation space by focusing on the unique characteristics of each class. We conducted experiments to validate the effectiveness of our proposed method using a limited number of labeled samples. The results demonstrate that our method effectively enhances performance, particularly in environments with limited labeled data, as evidenced by visual analysis.https://ieeexplore.ieee.org/document/10918676/Contrastive learningself-supervised learningsemi-supervised learning |
| spellingShingle | Mikyung Kang Sooyon Seo Moohong Min PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization IEEE Access Contrastive learning self-supervised learning semi-supervised learning |
| title | PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization |
| title_full | PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization |
| title_fullStr | PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization |
| title_full_unstemmed | PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization |
| title_short | PC-Match: Semi-Supervised Learning With Progressive Contrastive and Consistency Regularization |
| title_sort | pc match semi supervised learning with progressive contrastive and consistency regularization |
| topic | Contrastive learning self-supervised learning semi-supervised learning |
| url | https://ieeexplore.ieee.org/document/10918676/ |
| work_keys_str_mv | AT mikyungkang pcmatchsemisupervisedlearningwithprogressivecontrastiveandconsistencyregularization AT sooyonseo pcmatchsemisupervisedlearningwithprogressivecontrastiveandconsistencyregularization AT moohongmin pcmatchsemisupervisedlearningwithprogressivecontrastiveandconsistencyregularization |