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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
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
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
Learning with Noisy labels via Self-supervised Adversarial Noisy MaskingCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
On Learning Contrastive Representations for Learning with Noisy LabelsCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
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