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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 51–75 of 249 papers

TitleStatusHype
Improving Generalization by Controlling Label-Noise Information in Neural Network WeightsCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision TransformerCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Hard Sample Aware Noise Robust Learning for Histopathology Image ClassificationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Learning with Feature-Dependent Label Noise: A Progressive ApproachCode1
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image ClassificationCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
Regularly Truncated M-estimators for Learning with Noisy LabelsCode1
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