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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 201–249 of 249 papers

TitleStatusHype
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Robust Federated Learning with Noisy LabelsCode1
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
When Optimizing f-divergence is Robust with Label NoiseCode1
Learning with Instance-Dependent Label Noise: A Sample Sieve ApproachCode1
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels—0
Robust and On-the-fly Dataset Denoising for Image Classification—0
No Regret Sample Selection with Noisy LabelsCode0
Does label smoothing mitigate label noise?—0
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
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Confidence Scores Make Instance-dependent Label-noise Learning Possible—0
Searching to Exploit Memorization Effect in Learning with Noisy Labels—0
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis—0
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise—0
Meta Label Correction for Noisy Label LearningCode0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise RatesCode1
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
Symmetric Cross Entropy for Robust Learning with Noisy LabelsCode0
Deep Self-Learning From Noisy Labels—0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Are Anchor Points Really Indispensable in Label-Noise Learning?Code0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
Unifying semi-supervised and robust learning by mixup—0
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsCode0
Safeguarded Dynamic Label Regression for Generalized Noisy SupervisionCode0
How does Disagreement Help Generalization against Label Corruption?Code0
Learning to Learn from Noisy Labeled DataCode0
Limited Gradient Descent: Learning With Noisy Labels—0
SIGUA: Forgetting May Make Learning with Noisy Labels More RobustCode0
Randomized Wagering Mechanisms—0
Dimensionality-Driven Learning with Noisy LabelsCode0
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Joint Optimization Framework for Learning with Noisy LabelsCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Learning with Noisy Labels—0
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