Robust Semi-Supervised Learning in Open Environments
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917878691790848 |
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| author | Guo, Lan-Zhe Jia, Lin-Han Shao, Jie-Jing Li, Yu-Feng |
| author_facet | Guo, Lan-Zhe Jia, Lin-Han Shao, Jie-Jing Li, Yu-Feng |
| contents | Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where important factors (e.g., label, feature, distribution) between labeled and unlabeled data are consistent. However, more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent. It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation, even worse than the simple supervised learning baseline. Manually verifying the quality of unlabeled data is not desirable, therefore, it is important to study robust SSL with inconsistent unlabeled data in open environments. This paper briefly introduces some advances in this line of research, focusing on techniques concerning label, feature, and data distribution inconsistency in SSL, and presents the evaluation benchmarks. Open research problems are also discussed for reference purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Semi-Supervised Learning in Open Environments Guo, Lan-Zhe Jia, Lin-Han Shao, Jie-Jing Li, Yu-Feng Machine Learning Artificial Intelligence Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where important factors (e.g., label, feature, distribution) between labeled and unlabeled data are consistent. However, more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent. It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation, even worse than the simple supervised learning baseline. Manually verifying the quality of unlabeled data is not desirable, therefore, it is important to study robust SSL with inconsistent unlabeled data in open environments. This paper briefly introduces some advances in this line of research, focusing on techniques concerning label, feature, and data distribution inconsistency in SSL, and presents the evaluation benchmarks. Open research problems are also discussed for reference purposes. |
| title | Robust Semi-Supervised Learning in Open Environments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.18256 |