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Learning with noisy labels

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.

Papers

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Showing 111–120 of 249 papers

TitleStatusHype
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages—0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels—0
SELC: Self-Ensemble Label Correction Improves Learning with Noisy LabelsCode1
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative ModelCode1
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy LabelsCode1
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text ClassificationCode1
Few-shot Learning with Noisy LabelsCode1
UNICON: Combating Label Noise Through Uniform Selection and Contrastive LearningCode1
Scalable Penalized Regression for Noise Detection in Learning with Noisy LabelsCode1
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