{"total":97,"rows":[{"id":"2110.12088","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","created":"2021-10-22","cited_by_count":256,"area":null},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93,"area":null},{"id":"2106.00445","title":"Sample Selection with Uncertainty of Losses for Learning with Noisy Labels","created":"2021-06-01","cited_by_count":79,"area":null},{"id":"2102.05291","title":"Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels","created":"2021-02-10","cited_by_count":65,"area":null},{"id":"2110.06283","title":"Detecting Corrupted Labels Without Training a Model to Predict","created":"2021-10-12","cited_by_count":58,"area":null},{"id":"2109.02986","title":"Instance-dependent Label-noise Learning under a Structural Causal Model","created":"2021-09-07","cited_by_count":50,"area":null},{"id":"2206.02791","title":"Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation","created":"2022-06-06","cited_by_count":49,"area":null},{"id":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2510.00915","title":"Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers","created":"2025-10-01","cited_by_count":45,"area":"lr"},{"id":"2202.02016","title":"Identifiability of Label Noise Transition Matrix","created":"2022-02-04","cited_by_count":34,"area":null},{"id":"2105.13001","title":"Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network","created":"2021-05-27","cited_by_count":30,"area":null},{"id":"2202.01273","title":"Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features","created":"2022-02-02","cited_by_count":27,"area":null},{"id":"2309.17002","title":"Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks","created":"2023-09-29","cited_by_count":27,"area":null},{"id":"2102.05336","title":"Understanding Instance-Level Label Noise: Disparate Impacts and Treatments","created":"2021-02-10","cited_by_count":22,"area":null},{"id":"2111.14932","title":"Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection","created":"2021-11-29","cited_by_count":20,"area":null},{"id":"2204.02620","title":"Towards Robust Adaptive Object Detection under Noisy Annotations","created":"2022-04-06","cited_by_count":20,"area":null},{"id":"2203.15202","title":"SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation","created":"2022-03-29","cited_by_count":17,"area":null},{"id":"2402.17517","title":"Label-Noise Robust Diffusion Models","created":"2024-02-27","cited_by_count":16,"area":"diffusion"},{"id":"2310.18910","title":"InstanT: Semi-supervised Learning with Instance-dependent Thresholds","created":"2023-10-29","cited_by_count":13,"area":null},{"id":"2405.02648","title":"A Conformal Prediction Score that is Robust to Label Noise","created":"2024-05-04","cited_by_count":13,"area":null},{"id":"2306.03288","title":"Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization","created":"2023-06-05","cited_by_count":12,"area":null},{"id":"2308.13236","title":"Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin Memory","created":"2023-08-25","cited_by_count":11,"area":null},{"id":"2305.19395","title":"DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling","created":"2023-05-30","cited_by_count":10,"area":null},{"id":"2302.09595","title":"Latent Class-Conditional Noise Model","created":"2023-02-19","cited_by_count":9,"area":null},{"id":"2405.20494","title":"Slight Corruption in Pre-training Data Makes Better Diffusion Models","created":"2024-05-30","cited_by_count":9,"area":"diffusion"},{"id":"2206.07994","title":"Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels","created":"2022-06-16","cited_by_count":8,"area":null},{"id":"2309.00894","title":"Regularly Truncated M-estimators for Learning with Noisy Labels","created":"2023-09-02","cited_by_count":8,"area":null},{"id":"2312.06273","title":"Regroup Median Loss for Combating Label Noise","created":"2023-12-11","cited_by_count":8,"area":null},{"id":"2403.06869","title":"Impact of Noisy Supervision in Foundation Model Learning","created":"2024-03-11","cited_by_count":8,"area":null},{"id":"2106.00455","title":"Instance Correction for Learning with Open-set Noisy Labels","created":"2021-06-01","cited_by_count":7,"area":null},{"id":"2209.09500","title":"Reduction from Complementary-Label Learning to Probability Estimates","created":"2022-09-20","cited_by_count":7,"area":null},{"id":"2405.05714","title":"Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning","created":"2024-05-08","cited_by_count":7,"area":null},{"id":"2210.07636","title":"Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning","created":"2022-10-14","cited_by_count":6,"area":null},{"id":"2508.02387","title":"$\\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise","created":"2025-08-04","cited_by_count":6,"area":null},{"id":"2207.00118","title":"ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State","created":"2022-06-30","cited_by_count":5,"area":null},{"id":"2211.11753","title":"SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels","created":"2022-11-20","cited_by_count":5,"area":null},{"id":"2305.19391","title":"Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach","created":"2023-05-30","cited_by_count":5,"area":null},{"id":"2401.01650","title":"De-Confusing Pseudo-Labels in Source-Free Domain Adaptation","created":"2024-01-03","cited_by_count":5,"area":null},{"id":"2402.04398","title":"Learning under Temporal Label Noise","created":"2024-02-06","cited_by_count":5,"area":null},{"id":"2501.06678","title":"Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels","created":"2025-01-12","cited_by_count":5,"area":null},{"id":"2501.12749","title":"Conformal Prediction of Classifiers with Many Classes based on Noisy Labels","created":"2025-01-22","cited_by_count":5,"area":null},{"id":"2501.18363","title":"Exploring the Noise Robustness of Online Conformal Prediction","created":"2025-01-30","cited_by_count":5,"area":null},{"id":"2305.07892","title":"DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning","created":"2023-05-13","cited_by_count":4,"area":null},{"id":"2307.05898","title":"Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation","created":"2023-07-12","cited_by_count":4,"area":null},{"id":"2403.02690","title":"Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning","created":"2024-03-05","cited_by_count":4,"area":null},{"id":"2406.18865","title":"From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions","created":"2024-06-27","cited_by_count":4,"area":null},{"id":"2203.10858","title":"Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation","created":"2022-03-21","cited_by_count":3,"area":null},{"id":"2402.01055","title":"Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures","created":"2024-02-01","cited_by_count":3,"area":null},{"id":"2402.06019","title":"Checking the Sufficiently Scattered Condition using a Global Non-Convex Optimization Software","created":"2024-02-08","cited_by_count":3,"area":null},{"id":"2512.09315","title":"Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook","created":"2025-12-10","cited_by_count":3,"area":null},{"id":"2201.12739","title":"Do We Need to Penalize Variance of Losses for Learning with Label Noise?","created":"2022-01-30","cited_by_count":2,"area":null},{"id":"2208.10683","title":"Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling","created":"2022-08-23","cited_by_count":2,"area":null},{"id":"2210.05126","title":"Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration","created":"2022-10-11","cited_by_count":2,"area":null},{"id":"2306.11911","title":"LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2503.16976","title":"GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation","created":"2025-03-21","cited_by_count":2,"area":null},{"id":"2504.02996","title":"Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2504.06805","title":"Robust Classification with Noisy Labels Based on Posterior Maximization","created":"2025-04-09","cited_by_count":2,"area":null},{"id":"2301.01405","title":"Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach","created":"2023-01-04","cited_by_count":1,"area":null},{"id":"2307.13100","title":"Label Noise: Correcting the Forward-Correction","created":"2023-07-24","cited_by_count":1,"area":null},{"id":"2308.01184","title":"Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation","created":"2023-08-02","cited_by_count":1,"area":null},{"id":"2312.07052","title":"Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography","created":"2023-12-12","cited_by_count":1,"area":null},{"id":"2407.07331","title":"Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction","created":"2024-07-10","cited_by_count":1,"area":null},{"id":"2409.06830","title":"Noisy Early Stopping for Noisy Labels","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2502.09363","title":"The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2502.14281","title":"Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts","created":"2025-02-20","cited_by_count":1,"area":null},{"id":"2504.09330","title":"Regretful Decisions under Label Noise","created":"2025-04-12","cited_by_count":1,"area":null},{"id":"2505.22028","title":"Weakly-Supervised Contrastive Learning for Imprecise Class Labels","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2510.08179","title":"Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2603.12997","title":"Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis","created":"2026-03-13","cited_by_count":1,"area":null},{"id":"2605.20347","title":"Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2106.07217","title":"Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels","created":"2021-06-14","cited_by_count":0,"area":null},{"id":"2202.13203","title":"Dropout can Simulate Exponential Number of Models for Sample Selection Techniques","created":"2022-02-26","cited_by_count":0,"area":null},{"id":"2206.13025","title":"Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels","created":"2022-06-27","cited_by_count":0,"area":null},{"id":"2301.00524","title":"Learning Confident Classifiers in the Presence of Label Noise","created":"2023-01-02","cited_by_count":0,"area":null},{"id":"2309.12706","title":"Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm","created":"2023-09-22","cited_by_count":0,"area":null},{"id":"2311.16580","title":"Clean Label Disentangling for Medical Image Segmentation with Noisy Labels","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2312.00827","title":"A Unified Framework for Connecting Noise Modeling to Boost Noise Detection","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2412.00452","title":"Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels","created":"2024-11-30","cited_by_count":0,"area":null},{"id":"2505.13342","title":"Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2507.11807","title":"CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2508.03108","title":"Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2508.06622","title":"Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2509.06918","title":"Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2510.03016","title":"Learning Robust Diffusion Models from Imprecise Supervision","created":"2025-10-03","cited_by_count":0,"area":"diffusion"},{"id":"2511.04291","title":"Robustness of Minimum-Volume Nonnegative Matrix Factorization under an Expanded Sufficiently Scattered Condition","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2601.03875","title":"Staged Voxel-Level Deep Reinforcement Learning for 3D Medical Image Segmentation with Noisy Annotations","created":"2026-01-07","cited_by_count":0,"area":"deeprl"},{"id":"2601.12795","title":"Combating Noisy Labels through Fostering Self- and Neighbor-Consistency","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2603.21153","title":"Learning from Label Proportions with Dual-proportion Constraints","created":"2026-03-22","cited_by_count":0,"area":null},{"id":"2604.06468","title":"Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2605.16081","title":"MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.20721","title":"Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrix","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2607.16293","title":"SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.18465","title":"AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2607.23865","title":"XMix: Combating Extremely Noisy Labels via Local Smoothness in Self-Supervised Feature Space","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2607.24921","title":"Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2607.24943","title":"Multiclass Classification without Labels via Posterior Simplex Geometry","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2609.39829","title":"Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}