Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions
Samples in large-scale datasets may be mislabeled for various reasons, and deep models are inclined to over-fit some noisy samples using conventional training procedures. The key solution is to alleviate the harm of these noisy labels. Many existing methods try to divide training data into clean and...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3369 |
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| author | Lida Yu Xuefeng Liang Chang Cao Longshan Yao Xingyu Liu |
| author_facet | Lida Yu Xuefeng Liang Chang Cao Longshan Yao Xingyu Liu |
| author_sort | Lida Yu |
| collection | DOAJ |
| description | Samples in large-scale datasets may be mislabeled for various reasons, and deep models are inclined to over-fit some noisy samples using conventional training procedures. The key solution is to alleviate the harm of these noisy labels. Many existing methods try to divide training data into clean and noisy subsets in terms of loss values. We observe that a reason hindering the better performance of deep models is the uncertain samples, which have relatively small losses and often appear in real-world datasets. Due to small losses, many uncertain noisy samples are divided into the clean subset and then degrade models’ performance. Instead, we propose a Tripartite solution to partition training data into three subsets, <i>uncertain</i>, <i>clean</i> and <i>noisy</i> according to the following criteria: the inconsistency of the predictions of two networks and the given labels. Tripartite considerably improves the quality of the clean subset. Moreover, to maximize the value of clean samples in the uncertain subset and minimize the harm of noisy labels, we apply low-weight learning and a semi-supervised learning, respectively. Extensive experiments demonstrate that Tripartite can filter out noisy samples more precisely and outperforms most state-of-the-art methods on four benchmark datasets and especially real-world datasets. |
| format | Article |
| id | doaj-art-fbe5b4ebaece48cab6fc3f9a8ee5dd3c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fbe5b4ebaece48cab6fc3f9a8ee5dd3c2025-08-20T03:46:46ZengMDPI AGSensors1424-82202025-05-012511336910.3390/s25113369Tripartite: Tackling Realistic Noisy Labels with More Precise PartitionsLida Yu0Xuefeng Liang1Chang Cao2Longshan Yao3Xingyu Liu4School of Arts and Sciences, Beijing Normal University, Beijing 100875, ChinaSchool of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaSchool of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaSamples in large-scale datasets may be mislabeled for various reasons, and deep models are inclined to over-fit some noisy samples using conventional training procedures. The key solution is to alleviate the harm of these noisy labels. Many existing methods try to divide training data into clean and noisy subsets in terms of loss values. We observe that a reason hindering the better performance of deep models is the uncertain samples, which have relatively small losses and often appear in real-world datasets. Due to small losses, many uncertain noisy samples are divided into the clean subset and then degrade models’ performance. Instead, we propose a Tripartite solution to partition training data into three subsets, <i>uncertain</i>, <i>clean</i> and <i>noisy</i> according to the following criteria: the inconsistency of the predictions of two networks and the given labels. Tripartite considerably improves the quality of the clean subset. Moreover, to maximize the value of clean samples in the uncertain subset and minimize the harm of noisy labels, we apply low-weight learning and a semi-supervised learning, respectively. Extensive experiments demonstrate that Tripartite can filter out noisy samples more precisely and outperforms most state-of-the-art methods on four benchmark datasets and especially real-world datasets.https://www.mdpi.com/1424-8220/25/11/3369realistic noisy labeluncertain samplestripartitionbipartition |
| spellingShingle | Lida Yu Xuefeng Liang Chang Cao Longshan Yao Xingyu Liu Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions Sensors realistic noisy label uncertain samples tripartition bipartition |
| title | Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions |
| title_full | Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions |
| title_fullStr | Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions |
| title_full_unstemmed | Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions |
| title_short | Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions |
| title_sort | tripartite tackling realistic noisy labels with more precise partitions |
| topic | realistic noisy label uncertain samples tripartition bipartition |
| url | https://www.mdpi.com/1424-8220/25/11/3369 |
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