Noisy-Label Learning

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6 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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Latest papers 63

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Scaling Learning-based AEB with Massive Unlabeled Data
Jun 17, 2026Xiangyu Wang, Yang Zhan, Mengxiang Hao +9Collision Avoidance Autonomous Driving
All authors are with Li Auto.
Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier
Jun 15, 2026S. A. K. Leeney, W. J. Handley, H. T. J. Bevins +1Neural Network Robustness Noisy-Label Learning
Astrophysics Group, Cavendish Laboratory University of Cambridge · Institute of Astronomy University of Cambridge
Benchmarking Instance-Dependent Label Noise with Controlled Corruptions
Jun 12, 2026Shadman Islam, Agustinus Kristiadi, Mostafa MilaniSynthetic Benchmark Generation Noisy-Label Learning
Department of Computer Science, Western University, London, Ontario, Canada
Limits of spectral learning under noise
Jun 11, 2026Sabin Roman, Ljupco Todorovski, Saso Dzeroski +2Noisy-Label Learning Spectral Methods
Jožef Stefan Institute, Ljubljana, Slovenia · Faculty of Mathematics and Physics, University of Ljubljana, Slovenia · Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia +2
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Jun 10, 2026Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +3Training Data Curation Noisy-Label Learning
Noise-Aware Framework for Correcting Corrupted Labels
Jun 10, 2026Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4Training Data Curation Noisy-Label Learning
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
Efficiently Learning Drifting Halfspaces with Massart Noise
Jun 9, 2026Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1Noisy-Label Learning Learning with Noisy Labels
University of Wisconsin-Madison
An Adaptive Data cleaning Framework for Noisy Label Detection
Jun 5, 2026Chen-Hsuan Fang, Wei-Hsinag Chen, Pin-Hsuan Yu +2Noisy-Label Learning Learning with Noisy Labels
Department of Electrical Eng. National Taiwan Ocean University, Keelung City 20224, Taiwan · AI Research Center, National Taiwan Ocean University, Keelung City 20224, Taiwan
On Revisiting Entropy for Identifying Mislabeled Images
May 29, 2026Chunlei Li, Zixuan Zheng, Yilei Shi +5Noisy-Label Learning
MedAI Technology (Wuxi) Co. Ltd., Wuxi, China · Sichuan University, Chengdu, China · University of Basel, Allschwil, Switzerland +1
Data filtering methods for training language models
May 28, 2026Egor Shevchenko, Elena BruchesNoisy-Label Learning
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
May 27, 2026Safal Thapaliya, Jiatan Huang, Chuxu ZhangLLM-Assisted Annotation Text-Attributed Graph Learning
University of Connecticut, USA
Noise-Robust Financial Numerical Entity Attribute Tagging
May 24, 2026Hsin-Min Lu, Chen-Yang Lai, Yi-Jhen Li +1Document Information Extraction Noisy-Label Learning
National Taiwan University · National Central University
From Theory to Decision Rule: Calibrating the Noisy-Label Crossover for Vision-Language Model Weak Supervision Across Three Medical-Imaging Benchmarks
May 23, 2026Bruce Changlong Xu, Jose James, Alexander RyuVision-Language Models Medical Image Classification
Department of Computer Science, Stanford University, Stanford, CA, USA · Mayo Clinic, Rochester, MN, USA.
Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure
May 21, 2026Zitong Li, Haoyu WangVision Foundation Model Adaptation Noisy-Label Learning
Department of Biostatistics and Health Informatics · King's College London
Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
May 20, 2026Zongyu Li, Xuanyu Liu, Gongce Cao +3Noisy-Label Learning Recommender Systems
Guangdong University of Technology, Guangzhou, China · University of Chinese Academy of Sciences, Beijing, China · Capital Normal University, Dalian, China +2
Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels
May 19, 2026Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe GiguèreNeural Network Robustness Noisy-Label Learning
Department of Computer Science and Software Engineering Université Laval, Québec, Canada
Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
May 18, 2026Ba Hoang Anh Nguyen, Viet Cuong TaMeta-Learning Noisy-Label Learning
Human-Machine Interaction Laboratory, VNU University of Engineering and Technology, Hanoi, Vietnam
Robust Audio Tagging under Class-wise Supervision Unreliability
May 17, 2026Yuanbo Hou, Zhaoyi Liu, Tong Ye +4Noisy-Label Learning Weakly Supervised Learning
Machine Learning Research Group, Engineering Science, University of Oxford, UK · KU Leuven, Belgium · GISP, Harbin Engineering University, China +2
Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
May 17, 2026Ningkang Peng, Jingyang Mao, Xiaoqian Peng +2Noisy-Label Learning Gradient Alignment
Nanjing Normal University · Nanjing University of Chinese Medicine
Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
May 12, 2026Luca Parolari, Pietro Gori, Lamberto Ballan +2Contrastive Learning Endoscopy
Department of Mathematics, University of Padova, Padova, Italy · LTCI, Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France · Cosmo Intelligent Medical Devices, Dublin, Ireland
SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels
May 11, 2026Zahra Hafezi Kafshgari, Hadi Hadizadeh, Parvaneh SaeediSplit Federated Learning Noisy-Label Learning
School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
May 11, 2026Inhyuk Park, Doohyun ParkNoisy-Label Learning Learning with Noisy Labels
VUNO Inc.
Disagreement-Regularized Importance Sampling for Adversarial Label Corruption
May 8, 2026Csongor Horváth, Ida-Maria Sintorn, Prashant SinghImportance Sampling Noisy-Label Learning
Department of Information Technology, Uppsala University, Uppsala, Sweden · Science for Life Laboratory, Uppsala University, Uppsala, Sweden
Optimal Transport for LLM Reward Modeling from Noisy Preference
May 7, 2026Licheng Pan, Haochen Yang, Haoxuan Li +8Reward Modeling Noisy-Label Learning
Zhejiang University · Xiaohongshu Inc · National University of Singapore +2
Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers
May 7, 2026Frederik Schäfer, Luis Mandl, Lars Kälber +1Bayesian Neural Networks Vision Transformer
Institute of Structural Mechanics and Dynamics in Aerospace Engineering, University of Stuttgart · Rockwell Collins Deutschland GmbH · Department of Hepatobiliary Surgery and Visceral Transplantation, University of Leipzig Medical Center
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
May 6, 2026Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7Federated Learning Aggregation Label Distribution Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · University of the Chinese Academy of Sciences, China +3
Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models
May 1, 2026Jiayu Li, Jiaxin Qi, Sheng Zhou +2VLM Robustness VLM Adaptation
Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization
Apr 27, 2026Xinxin Liu, Ming Li, Zonglin Lyu +2Pairwise Preference Learning Diffusion Model Alignment
University of Central Florida
FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
Apr 22, 2026Sina Gholami, Abdulmoneam Ali, Tania Haghighi +2Noisy-Label Learning Learning with Noisy Labels
University of North Carolina at Charlotte Charlotte, NC, USA
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
Apr 21, 2026Abdulmoneam Ali, Ahmed ArafaClustering Noisy-Label Learning
Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA

Jun 17, 2026, cs.LGJ/K move · Enter open · S save

Scaling Learning-based AEB with Massive Unlabeled Data

Xiangyu Wang, Yang Zhan, Mengxiang Hao, Chuanchuan Zhong+8

All authors are with Li Auto.

Collision Avoidance Autonomous Driving Noisy-Label Learning Pseudo-Labeling Semi-Supervised Learning

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This paper studies how to scale learning-based automatic emergency braking (AEB) with massive unlabeled fleet data under production constraints. Our approach is based on meta-feedback semi-supervised learning (MF-SSL), where a teacher generates pseudo labels for unlabeled driving data and is updated using a small labeled anchor set as safety-critical feedback. In production, anchor ambiguity and labeled-unlabeled mismatch can amplify systematic pseudo-label errors, leading to spurious triggers. We propose a stabilized MF-SSL framework with (i) Noise-Aware Decoupling, which removes ambiguity-prone anchors from the teacher's supervised update path, and (ii) kinematics-gated pseudo-labeling with a teacher conflict penalty to suppress mismatch-induced risk hallucinations on unlabeled data while maintaining broad coverage. Extensive experiments show consistent gains as unlabeled data scale from 1M to 1B windows, improving safety while keeping comfort stable. The 1B-trained student model is deployed to hundreds of thousands of vehicles and validated over $10^9$ km of driving, achieving a positive-to-false activation ratio exceeding 100:1 and a 35% improvement in accident-free driving mileage over a production rule-only baseline.

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Latest papers 63