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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 226–249 of 249 papers

TitleStatusHype
Meta Label Correction for Noisy Label LearningCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
Symmetric Cross Entropy for Robust Learning with Noisy LabelsCode0
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
Limited Gradient Descent: Learning With Noisy Labels—0
SIGUA: Forgetting May Make Learning with Noisy Labels More RobustCode0
Randomized Wagering Mechanisms—0
Dimensionality-Driven Learning with Noisy LabelsCode0
Joint Optimization Framework for Learning with Noisy LabelsCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Learning with Noisy Labels—0
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