Probabilistic instance dependent label refinement for noisy label learning

Label refinement methods are designed to improve the quality of training labels by incorporating model predictions into the original training labels. By adjusting the combination coefficient of the noisy label, the impact of noise is reduced, which in turn makes the training process more robust. How...

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Veröffentlicht in:	Machine learning Jg. 114; H. 5; S. 120
Hauptverfasser:	He, Hao-Yuan, Liu, Yu, Liu, Ren-Biao, Xie, Zheng, Li, Ming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computer Science Control Labels Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Noise generation Optimization Probability Probability theory Robotics Simulation and Modeling Statistical analysis Deep neural networks Instance-dependent noise Machine learning Classification Noisy label learning
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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