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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 1–25 of 249 papers

TitleStatusHype
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
NLPrompt: Noise-Label Prompt Learning for Vision-Language ModelsCode2
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
Asymmetric Loss Functions for Learning with Noisy LabelsCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Bayesian Optimization Meets Self-DistillationCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
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