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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
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Few-shot Learning with Noisy LabelsCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative ModelCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
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