Learning With Partial-Label and Unlabeled Data: Contrastive With Negative Example Separation
Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. In SSPLL, the existence of unlabeled examples presents a great challenge to train a model with good generalization ability....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11098874/ |
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