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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
Selective-Supervised Contrastive Learning with Noisy LabelsCode1
On Learning Contrastive Representations for Learning with Noisy LabelsCode1
Robust Training under Label Noise by Over-parameterizationCode1
PENCIL: Deep Learning with Noisy Labels—0
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Learning with Neighbor Consistency for Noisy Labels—0
Identifiability of Label Noise Transition Matrix—0
Do We Need to Penalize Variance of Losses for Learning with Label Noise?—0
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
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