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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 1–50 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
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal RetrievalCode1
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
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision TransformerCode1
ProMix: Combating Label Noise via Maximizing Clean Sample UtilityCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
Compressing Features for Learning with Noisy LabelsCode1
Protoformer: Embedding Prototypes for TransformersCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative ModelCode1
SELC: Self-Ensemble Label Correction Improves Learning with Noisy LabelsCode1
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy LabelsCode1
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text ClassificationCode1
Few-shot Learning with Noisy LabelsCode1
UNICON: Combating Label Noise Through Uniform Selection and Contrastive LearningCode1
Scalable Penalized Regression for Noise Detection in Learning with Noisy LabelsCode1
Selective-Supervised Contrastive Learning with Noisy LabelsCode1
On Learning Contrastive Representations for Learning with Noisy LabelsCode1
Robust Training under Label Noise by Over-parameterizationCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Hard Sample Aware Noise Robust Learning for Histopathology Image ClassificationCode1
Sample Prior Guided Robust Model Learning to Suppress Noisy LabelsCode1
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Understanding Generalized Label Smoothing when Learning with Noisy LabelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
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