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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 151–200 of 249 papers

TitleStatusHype
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Cooperative Learning for Noisy Supervision—0
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
An Instance-Dependent Simulation Framework for Learning with Label NoiseCode0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
Mitigating Memorization in Sample Selection for Learning with Noisy Labels—0
Understanding and Improving Early Stopping for Learning with Noisy LabelsCode1
Distilling effective supervision for robust medical image segmentation with noisy labels—0
Open-set Label Noise Can Improve Robustness Against Inherent Label NoiseCode1
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
DualGraph: A Graph-Based Method for Reasoning About Label Noise—0
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels—0
To Smooth or Not? When Label Smoothing Meets Noisy LabelsCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Joint Text and Label Generation for Spoken Language Understanding—0
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
Transform consistency for learning with noisy labels—0
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?Code0
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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