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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
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Meta Label Correction for Noisy Label LearningCode0
Can We Treat Noisy Labels as Accurate?Code0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy LabelsCode0
No Regret Sample Selection with Noisy LabelsCode0
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