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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 91–100 of 249 papers

TitleStatusHype
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
Learning with Noisy Labels—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
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