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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 231–240 of 249 papers

TitleStatusHype
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Dimensionality-Driven Learning with Noisy LabelsCode0
Safeguarded Dynamic Label Regression for Generalized Noisy SupervisionCode0
No Regret Sample Selection with Noisy LabelsCode0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Early Stopping Against Label Noise Without Validation DataCode0
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation LearningCode0
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