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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 11–20 of 249 papers

TitleStatusHype
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Bayesian Optimization Meets Self-DistillationCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
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