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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 201–210 of 249 papers

TitleStatusHype
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Robust Federated Learning with Noisy LabelsCode1
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
When Optimizing f-divergence is Robust with Label NoiseCode1
Learning with Instance-Dependent Label Noise: A Sample Sieve ApproachCode1
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
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