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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 171–180 of 249 papers

TitleStatusHype
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial ImagesCode1
Transform consistency for learning with noisy labels—0
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Co-matching: Combating Noisy Labels by Augmentation Anchoring—0
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels—0
Learning with Group Noise—0
Learning with Feature-Dependent Label Noise: A Progressive ApproachCode1
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