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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 111–120 of 249 papers

TitleStatusHype
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Mitigating Label Noise on Graph via Topological Sample SelectionCode0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
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