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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 226–249 of 249 papers

TitleStatusHype
Dynamic Loss For Robust LearningCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Learning to Learn from Noisy Labeled DataCode0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language ProcessingCode0
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Dimensionality-Driven Learning with Noisy LabelsCode0
Safeguarded Dynamic Label Regression for Generalized Noisy SupervisionCode0
No Regret Sample Selection with Noisy LabelsCode0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Early Stopping Against Label Noise Without Validation DataCode0
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation LearningCode0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Enhanced Meta Label Correction for Coping with Label CorruptionCode0
A Unified Framework for Connecting Noise Modeling to Boost Noise DetectionCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Benchmarking Label Noise in Instance Segmentation: Spatial Noise MattersCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
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