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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 211–220 of 249 papers

TitleStatusHype
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels—0
Robust and On-the-fly Dataset Denoising for Image Classification—0
No Regret Sample Selection with Noisy LabelsCode0
Does label smoothing mitigate label noise?—0
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Improving Generalization by Controlling Label-Noise Information in Neural Network WeightsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Confidence Scores Make Instance-dependent Label-noise Learning Possible—0
Searching to Exploit Memorization Effect in Learning with Noisy Labels—0
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