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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 191–200 of 249 papers

TitleStatusHype
Towards Robustness to Label Noise in Text Classification via Noise ModelingCode1
Unsupervised Domain Adaptation of Black-Box Source ModelsCode0
Towards Robust Graph Neural Networks against Label Noise—0
Noise against noise: stochastic label noise helps combat inherent label noise—0
ME-MOMENTUM: EXTRACTING HARD CONFIDENT EXAMPLES FROM NOISILY LABELED DATA—0
Robust early-learning: Hindering the memorization of noisy labels—0
Robust Collaborative Learning with Noisy Labels—0
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?—0
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data—0
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
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