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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 31–40 of 249 papers

TitleStatusHype
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Compressing Features for Learning with Noisy LabelsCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
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