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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 51–100 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
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Improving Generalization by Controlling Label-Noise Information in Neural Network WeightsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise RatesCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy—0
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization—0
Learning from Noisy Labels with Contrastive Co-Transformer—0
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples—0
Early Stopping Against Label Noise Without Validation DataCode0
Learning with Noisy Labels: the Exploration of Error Bounds in Classification—0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
Label Calibration in Source Free Domain Adaptation—0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
In-Context Learning with Noisy Labels—0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
May the Forgetting Be with You: Alternate Replay for Learning with Noisy LabelsCode0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
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