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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
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
Fine-Grained Classification with Noisy Labels—0
Latent Class-Conditional Noise ModelCode0
Learning with Noisy labels via Self-supervised Adversarial Noisy MaskingCode1
When Source-Free Domain Adaptation Meets Learning with Noisy Labels—0
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels—0
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels—0
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples—0
How To Prevent the Continuous Damage of Noises To Model Training?—0
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal RetrievalCode1
OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels—0
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels—0
Model and Data Agreement for Learning with Noisy LabelsCode0
Dynamic Loss For Robust LearningCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels—0
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method—0
Learning with Noisy Labels over Imbalanced Subpopulations—0
Learning advisor networks for noisy image classificationCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
Instance-Dependent Noisy Label Learning via Graphical ModellingCode1
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence PenalizationCode1
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