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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 76–100 of 249 papers

TitleStatusHype
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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