A Dynamic Mixture Noise Identification Method for Learning with Instance-dependent Label Noise

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A Dynamic Mixture Noise Identification Method for Learning with Instance-dependent Label Noise

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[1]JIANG Gaoxia,ZHANG Yao,WANG Wenjian.A Dynamic Mixture Noise Identification Method for Learning with Instance-dependent Label Noise[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):67-75.[doi:10.13705/j.issn.1671-6833.2025.06.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 67-75 Column: Public date: 2026-05-27

Title:: A Dynamic Mixture Noise Identification Method for Learning with Instance-dependent Label Noise

Author(s):: JIANG Gaoxia¹, ZHANG Yao¹, WANG Wenjian^1,2; 1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China; 2.Key Laboratory of Data Intelligence and Cognitive Computing of Shanxi Province, Taiyuan 030006, China

Keywords:: instance-dependent noise; learning with noisy label; class centroid; dynamic mixture model; semi-supervised learning

CLC:: TP181:TP183

DOI:: 10.13705/j.issn.1671-6833.2025.06.009

Abstract:: In learning with instance-dependent label noise (IDN), semi-supervised methods could mitigate noise interference and leverage feature information, but their effectiveness depended on accurate noise identification and was susceptible to the choice of recognition technique. To address this limitation, a robust feature-centroid mechanism was designed to weaken the influence of unreliable samples and a distribution-adaptive dynamic mixture model (DMM) was proposed based on feature similarity. Pairwise feature similarities was extracted, both Gaussian Mixture Models (GMM) and Beta Mixture Models (BMM) were used to fit these similarity distributions, and dynamically to fuse their outputs to achieve more accurate noise identification. A semi-supervised learning strategy was then integrated to complete the training process. On artificially corrupted CIFAR-10 and CIFAR-100 datasets, our method achieved state-of-the-art performance. On real-world noisy benchmarks Animal-10N and Clothing1M, it attained classification accuracies of 84.21% and 75.80%, respectively, outperforming representative existing approaches and demonstrating the effectiveness and applicability of our approach for IDN learning tasks.

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Last Update: 2026-05-27