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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 51–75 of 249 papers

TitleStatusHype
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Learning with Noisy Labels via Sparse RegularizationCode1
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
Understanding and Improving Early Stopping for Learning with Noisy LabelsCode1
Open-set Label Noise Can Improve Robustness Against Inherent Label NoiseCode1
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
To Smooth or Not? When Label Smoothing Meets Noisy LabelsCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial ImagesCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Learning with Feature-Dependent Label Noise: A Progressive ApproachCode1
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image ClassificationCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
FINE Samples for Learning with Noisy LabelsCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Provably End-to-end Label-Noise Learning without Anchor PointsCode1
Towards Robustness to Label Noise in Text Classification via Noise ModelingCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Robust Federated Learning with Noisy LabelsCode1
When Optimizing f-divergence is Robust with Label NoiseCode1
Learning with Instance-Dependent Label Noise: A Sample Sieve ApproachCode1
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