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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 221–230 of 249 papers

TitleStatusHype
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
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
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