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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 61–70 of 249 papers

TitleStatusHype
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Learning with Noisy Labels via Sparse RegularizationCode1
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