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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.09929 |
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| _version_ | 1866911822047608832 |
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| author | Kutt, Brody Ramteke, Pralay Mignot, Xavier Toman, Pamela Ramanan, Nandini Chhetri, Sujit Rokka Huang, Shan Du, Min Hewlett, William |
| author_facet | Kutt, Brody Ramteke, Pralay Mignot, Xavier Toman, Pamela Ramanan, Nandini Chhetri, Sujit Rokka Huang, Shan Du, Min Hewlett, William |
| contents | Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09929 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Contrastive Credibility Propagation for Reliable Semi-Supervised Learning Kutt, Brody Ramteke, Pralay Mignot, Xavier Toman, Pamela Ramanan, Nandini Chhetri, Sujit Rokka Huang, Shan Du, Min Hewlett, William Machine Learning Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown. |
| title | Contrastive Credibility Propagation for Reliable Semi-Supervised Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2211.09929 |