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OMDMix: Semi-Supervised Learning for Addressing Noisy Labels

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Guanyu Chen
Ruihao Li
Defu Liu
Wei Yi
Published in
Advanced Intelligent Computing Technology and Applications
Publisher
Springer Nature Singapore

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Abstract

The chapter explores the OMDMix framework, a novel approach to tackle the challenge of learning with noisy labels (LNL) in deep learning. The key topics covered include the introduction of the One-Miss-Drop (OMD) algorithm for selecting clean samples, the Dynamic Sample Selection (DSS) method for expanding the clean sample set, and the use of an improved dual-network MixMatch method for semi-supervised learning. The text also delves into related work on LNL and semi-supervised learning, providing a comprehensive overview of existing methods and their limitations. Experimental results demonstrate the effectiveness of OMDMix on both synthetic and real-world datasets, showcasing its superiority over state-of-the-art methods. The chapter concludes with an ablation study that highlights the importance of each component in the OMDMix framework, emphasizing the critical role of OMD in providing reliable initial supervision.
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previous chapter BDGTA: A Hybrid Neural Architecture for Enhanced RFID Indoor Positioning Using Bidirectional GRU and Sparse Attention Mechanisms
next chapter FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking
Title
OMDMix: Semi-Supervised Learning for Addressing Noisy Labels
Authors
Guanyu Chen
Ruihao Li
Defu Liu
Wei Yi
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Advanced Intelligent Computing Technology and Applications

Print ISBN: 978-981-9698-04-2

Electronic ISBN: 978-981-9698-05-9

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-96-9805-9_23
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