Concepedia

Concept

learning with noisy labels

Parents

46

Publications

2.5K

Citations

201

Authors

62

Institutions

About

Learning with noisy labels is a subfield within machine learning and data science focused on developing algorithms and techniques to train robust models effectively using datasets where the assigned labels for training instances contain errors or inaccuracies. This research area investigates methods to mitigate the detrimental effects of label noise on model performance, generalization, and reliability, encompassing approaches such as noise-robust loss functions, noise-aware training strategies, label correction, and noise rate estimation, which is critical for deploying machine learning systems in real-world applications where obtaining perfectly clean labeled data is challenging or cost-prohibitive.

Top Authors

Rankings shown are based on concept H-Index.

BH

Hong Kong Baptist University

TM

Stanford University

DD

Peking University

LZ

University of Washington

TL

The University of Sydney

Top Institutions

Rankings shown are based on concept H-Index.

University of Washington

Seattle, United States

Peking University

Beijing, China

Tsinghua University

Beijing, China

University of California, Santa Barbara

Santa Barbara, United States