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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 201–210 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
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