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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 181–190 of 249 papers

TitleStatusHype
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?—0
Relative Instance Credibility Inference for Learning with Noisy Labels—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Cooperative Learning for Noisy Supervision—0
An Instance-Dependent Simulation Framework for Learning with Label Noise—0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
Mitigating Memorization in Sample Selection for Learning with Noisy Labels—0
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