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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
Improving Generalization by Controlling Label-Noise Information in Neural Network WeightsCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
On Learning Contrastive Representations for Learning with Noisy LabelsCode1
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial ImagesCode1
Neighborhood Collective Estimation for Noisy Label Identification and CorrectionCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
FINE Samples for Learning with Noisy LabelsCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Instance-Dependent Noisy Label Learning via Graphical ModellingCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise RatesCode1
ProMix: Combating Label Noise via Maximizing Clean Sample UtilityCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Provably End-to-end Label-Noise Learning without Anchor PointsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal RetrievalCode1
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Dimensionality-Driven Learning with Noisy LabelsCode0
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Mitigating Label Noise on Graph via Topological Sample SelectionCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Model and Data Agreement for Learning with Noisy LabelsCode0
Meta Label Correction for Noisy Label LearningCode0
May the Forgetting Be with You: Alternate Replay for Learning with Noisy LabelsCode0
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy LabelsCode0
Debiased Sample Selection for Combating Noisy LabelsCode0
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Can We Treat Noisy Labels as Accurate?Code0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
How does Disagreement Help Generalization against Label Corruption?Code0
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