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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 101–150 of 249 papers

TitleStatusHype
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Neighborhood Collective Estimation for Noisy Label Identification and CorrectionCode1
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision TransformerCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
ProMix: Combating Label Noise via Maximizing Clean Sample UtilityCode1
Compressing Features for Learning with Noisy LabelsCode1
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels—0
Protoformer: Embedding Prototypes for TransformersCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
To Aggregate or Not? Learning with Separate Noisy Labels—0
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages—0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels—0
SELC: Self-Ensemble Label Correction Improves Learning with Noisy LabelsCode1
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative ModelCode1
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
PENCIL: Deep Learning with Noisy Labels—0
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Learning with Neighbor Consistency for Noisy LabelsCode0
Identifiability of Label Noise Transition Matrix—0
Do We Need to Penalize Variance of Losses for Learning with Label Noise?—0
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
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
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels—0
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
Prototypical Classifier for Robust Class-Imbalanced Learning—0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Relative Instance Credibility Inference for Learning with Noisy Labels—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?—0
Understanding Generalized Label Smoothing when Learning with Noisy LabelsCode1
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Understanding Sharpness-Aware Minimization—0
Robust Temporal Ensembling for Learning with Noisy Labels—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
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