ORCID: https://orcid.org/0000-0003-2162-8107 und Hüllermeier, Eyke
ORCID: https://orcid.org/0000-0002-9944-4108
(February 2024):
Mitigating Label Noise through Data Ambiguation.
AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024.
Proceedings of the AAAI Conference on Artificial Intelligence.
Vol. 38, No. 12
pp. 13799-13807
[Video (MP4), 775MB]
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Abstract
Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this problem, including robust loss functions and more complex label correction approaches. Robust loss functions are appealing due to their simplicity, but typically lack flexibility, while label correction usually adds substantial complexity to the training setup. In this paper, we suggest to address the shortcomings of both methodologies by "ambiguating" the target information, adding additional, complementary candidate labels in case the learner is not sufficiently convinced of the observed training label. More precisely, we leverage the framework of so-called superset learning to construct set-valued targets based on a confidence threshold, which deliver imprecise yet more reliable beliefs about the ground-truth, effectively helping the learner to suppress the memorization effect. In an extensive empirical evaluation, our method demonstrates favorable learning behavior on synthetic and real-world noise, confirming the effectiveness in detecting and correcting erroneous training labels.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
URN: | urn:nbn:de:bvb:19-epub-118343-5 |
ISSN: | 2159-5399 |
Language: | English |
Item ID: | 118343 |
Date Deposited: | 25. Jun 2024 05:55 |
Last Modified: | 26. Nov 2024 17:28 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 160364472 |