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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 51–60 of 249 papers

TitleStatusHype
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Learning with Noisy Labels via Sparse RegularizationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
Understanding and Improving Early Stopping for Learning with Noisy LabelsCode1
Open-set Label Noise Can Improve Robustness Against Inherent Label NoiseCode1
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
To Smooth or Not? When Label Smoothing Meets Noisy LabelsCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
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