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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 151–160 of 249 papers

TitleStatusHype
A Simple yet Effective Baseline for Robust Deep 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
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels—0
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
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