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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 161–170 of 249 papers

TitleStatusHype
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels—0
To Aggregate or Not? Learning with Separate Noisy Labels—0
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages—0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels—0
PENCIL: Deep Learning with Noisy Labels—0
Identifiability of Label Noise Transition Matrix—0
Learning with Neighbor Consistency for Noisy Labels—0
Do We Need to Penalize Variance of Losses for Learning with Label Noise?—0
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