Asymmetric Loss Functions for Noise-Tolerant Learning : Theory and Applications
Supervised deep learning has achieved tremendous success in many computer vision tasks, which however is prone to overfit noisy labels. To mitigate the undesirable influence of noisy labels, robust loss functions offer a feasible approach to achieve noise-tolerant learning. In this work, we systemat...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 7 vom: 09. Juli, Seite 8094-8109
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| 1. Verfasser: |
Zhou, Xiong
(VerfasserIn) |
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| Weitere Verfasser: |
Liu, Xianming,
Zhai, Deming,
Jiang, Junjun,
Ji, Xiangyang |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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