Noisy Label Learning

Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval CVPR 2026 D^3FER: Dual Channel and Dual Branch Network for Robust Facial Expression Recognition under Dual Challenges CVPR 2026 Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation ACL 2026 Debiased Sample Selection for Learning with Noisy Labels CVPR 2026 Do We Need to Penalize Variance of Losses for Learning with Label Noise? JMLR 2026 FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients CVPR 2026 Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation MIDL 2026 PAUL: Uncertainty-Guided Partition and Augmentation for Robust Cross-View Geo-Localization under Noisy Correspondence CVPR 2026 Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning CVPR 2026 Testing Noise Assumptions of Learning Algorithms COLT 2026 AlleNoise - large-scale text classification benchmark dataset with real-world label noise AISTATS 2025 Dynamic Multiple High-order Correlations Fusion with Noise Filtering for Incomplete Multi-view Noisy-label Learning IJCAI 2025 Learning from biased positive-unlabeled data via threshold calibration AISTATS 2025 Partial-Label Learning with Conformal Candidate Cleaning UAI 2025 Trusted re-weighting for label distribution learning UAI 2024 How To Prevent the Continuous Damage of Noises To Model Training? CVPR 2023 Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking CVPR 2023 Mitigating Memorization of Noisy Labels via Regularization between Representations ICLR 2023 Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance EMNLP 2023 Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023 Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation AAAI 2022 Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation NeurIPS 2022 Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations ICLR 2022 Multi Label Loss Correction against Missing and Corrupted Labels ACML 2022 RoLNiP: Robust Learning Using Noisy Pairwise Comparisons ACML 2022