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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 26–50 of 249 papers

TitleStatusHype
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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