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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 121–130 of 249 papers

TitleStatusHype
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels—0
Co-matching: Combating Noisy Labels by Augmentation Anchoring—0
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels—0
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples—0
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Confidence Scores Make Instance-dependent Label-noise Learning Possible—0
Cooperative Learning for Noisy Supervision—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
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