label noise

Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models ICLR 2023 Binary Classification with Confidence Difference NeurIPS 2023 Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels WACV 2023 Corruption-Tolerant Algorithms for Generalized Linear Models AAAI 2023 CrossSplit: Mitigating Label Noise Memorization through Data Splitting ICML 2023 CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation NeurIPS 2023 CTW: Confident Time-Warping for Time-Series Label-Noise Learning IJCAI 2023 Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization ICLR 2023 Delving into Noisy Label Detection with Clean Data ICML 2023 DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and Correction CVPR 2023 Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm JMLR 2023 Fine-Grained Classification With Noisy Labels CVPR 2023 Generalization Guarantees of Self-Training of Halfspaces under Label Noise Corruption IJCAI 2023 Graph-Based Self-Learning for Robust Person Re-Identification WACV 2023 GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks ICML 2023 Guiding Pseudo-Labels With Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation CVPR 2023 Holistic Label Correction for Noisy Multi-Label Classification ICCV 2023 How To Prevent the Continuous Damage of Noises To Model Training? CVPR 2023 Identifiability of Label Noise Transition Matrix ICML 2023 Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions ACL 2023 Investigation of Music Emotion Recognition Based on Segmented Semi-Supervised Learning Interspeech 2023 Is Your Noise Correction Noisy? PLS: Robustness To Label Noise With Two Stage Detection WACV 2023 JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification EMNLP 2023 Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity ICML 2023 Label-Noise Learning with Intrinsically Long-Tailed Data ICCV 2023