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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 21–30 of 249 papers

TitleStatusHype
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
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