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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
Protoformer: Embedding Prototypes for TransformersCode1
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
Regularly Truncated M-estimators for Learning with Noisy LabelsCode1
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Robust Training under Label Noise by Over-parameterizationCode1
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
Sample Prior Guided Robust Model Learning to Suppress 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
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
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
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text ClassificationCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
On the Role of Label Noise in the Feature Learning ProcessCode1
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