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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 51–75 of 249 papers

TitleStatusHype
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
Regularly Truncated M-estimators for Learning with Noisy LabelsCode1
Late Stopping: Avoiding Confidently Learning from Mislabeled ExamplesCode0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Partial Label Supervision for Agnostic Generative Noisy Label LearningCode0
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels—0
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type PerspectiveCode0
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels—0
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy LabelsCode0
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels—0
MIMO Detection under Hardware Impairments: Learning with Noisy Labels—0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Linear Distance Metric Learning with Noisy Labels—0
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label ConfigurationsCode1
Enhanced Meta Label Correction for Coping with Label CorruptionCode0
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language ProcessingCode0
Bayesian Optimization Meets Self-DistillationCode1
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Learning with Noisy Labels through Learnable Weighting and Centroid SimilarityCode0
Twin Contrastive Learning with Noisy LabelsCode1
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