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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 176–200 of 249 papers

TitleStatusHype
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Co-matching: Combating Noisy Labels by Augmentation Anchoring—0
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels—0
Learning with Group Noise—0
Learning with Feature-Dependent Label Noise: A Progressive ApproachCode1
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image ClassificationCode1
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Unified Robust Training for Graph NeuralNetworks against Label Noise—0
Augmentation Strategies for Learning with Noisy LabelsCode1
DST: Data Selection and joint Training for Learning with Noisy Labels—0
FINE Samples for Learning with Noisy LabelsCode1
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments—0
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Provably End-to-end Label-Noise Learning without Anchor PointsCode1
[Re] Can gradient clipping mitigate label noise?—0
Towards Robustness to Label Noise in Text Classification via Noise ModelingCode1
Unsupervised Domain Adaptation of Black-Box Source ModelsCode0
Towards Robust Graph Neural Networks against Label Noise—0
Noise against noise: stochastic label noise helps combat inherent label noise—0
ME-MOMENTUM: EXTRACTING HARD CONFIDENT EXAMPLES FROM NOISILY LABELED DATA—0
Robust early-learning: Hindering the memorization of noisy labels—0
Robust Collaborative Learning with Noisy Labels—0
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?—0
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data—0
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
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