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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 131–140 of 249 papers

TitleStatusHype
Hard Sample Aware Noise Robust Learning for Histopathology Image ClassificationCode1
Sample Prior Guided Robust Model Learning to Suppress Noisy LabelsCode1
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels—0
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
Prototypical Classifier for Robust Class-Imbalanced Learning—0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
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