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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 201–225 of 249 papers

TitleStatusHype
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type PerspectiveCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
Meta Label Correction for Noisy Label LearningCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
Robust Loss Functions for Training Decision Trees with Noisy LabelsCode0
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy LabelsCode0
Joint Optimization Framework for Learning with Noisy LabelsCode0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Mitigating Label Noise on Graph via Topological Sample SelectionCode0
Symmetric Cross Entropy for Robust Learning with Noisy LabelsCode0
Model and Data Agreement for Learning with Noisy LabelsCode0
Debiased Sample Selection for Combating Noisy LabelsCode0
Latent Class-Conditional Noise ModelCode0
Late Stopping: Avoiding Confidently Learning from Mislabeled ExamplesCode0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
Unsupervised Domain Adaptation of Black-Box Source ModelsCode0
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality GuidanceCode0
Learning advisor networks for noisy image classificationCode0
Exploring the Robustness of In-Context Learning with Noisy LabelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
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