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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 231–240 of 249 papers

TitleStatusHype
Deep Self-Learning From Noisy Labels—0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
Unifying semi-supervised and robust learning by mixup—0
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsCode0
Safeguarded Dynamic Label Regression for Generalized Noisy SupervisionCode0
How does Disagreement Help Generalization against Label Corruption?Code0
Learning to Learn from Noisy Labeled DataCode0
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