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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 201–225 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
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
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis—0
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise—0
Meta Label Correction for Noisy Label LearningCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise RatesCode1
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