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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
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature DistributionsCode1
Learning with Instance-Dependent Label Noise: A Sample Sieve ApproachCode1
Learning with Noisy Labels Revisited: A Study Using Real-World Human AnnotationsCode1
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise RatesCode1
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Twin Contrastive 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
Learning with Noisy Labels via Sparse RegularizationCode1
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
Instance-Dependent Noisy Label Learning via Graphical ModellingCode1
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image ClassificationCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy LabelsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression RecognitionCode1
Protoformer: Embedding Prototypes for TransformersCode1
Provably End-to-end Label-Noise Learning without Anchor PointsCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Learning with Noisy Labels—0
Deep Self-Learning From Noisy Labels—0
Deep learning with noisy labels in medical prediction problems: a scoping review—0
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels—0
Identifiability of Label Noise Transition Matrix—0
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