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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 1–10 of 249 papers

TitleStatusHype
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy—0
On the Role of Label Noise in the Feature Learning ProcessCode1
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization—0
Learning from Noisy Labels with Contrastive Co-Transformer—0
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples—0
Early Stopping Against Label Noise Without Validation DataCode0
Learning with Noisy Labels: the Exploration of Error Bounds in Classification—0
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