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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 1–25 of 249 papers

TitleStatusHype
NLPrompt: Noise-Label Prompt Learning for Vision-Language ModelsCode2
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
On the Role of Label Noise in the Feature Learning ProcessCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
Regularly Truncated M-estimators for Learning with Noisy LabelsCode1
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label ConfigurationsCode1
Bayesian Optimization Meets Self-DistillationCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Twin Contrastive Learning with Noisy LabelsCode1
Learning with Noisy labels via Self-supervised Adversarial Noisy MaskingCode1
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal RetrievalCode1
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
Instance-Dependent Noisy Label Learning via Graphical ModellingCode1
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence PenalizationCode1
Neighborhood Collective Estimation for Noisy Label Identification and CorrectionCode1
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