Enhancing Supply Chain Defect Detection through Cross-Domain Generalization: A Deep Learning Approach

Authors

  • Sujin Yli
  • Deokyoon Choi

DOI:

https://doi.org/10.54097/

Keywords:

Deep learning, Supply chain, Defect detection, Cross-domain generalization

Abstract

Ensuring quality control in the supply chain is paramount for maintaining product integrity and customer satisfaction. However, the scarcity of defect-specific data poses significant challenges for effective defect detection using traditional machine learning models. This paper introduces a novel Cross-Domain Generalization (CDG) methodology that integrates Cross-domain Augmentation, Multi-task Learning, and Iteration Learning to address data limitations and improve defect detection accuracy. Leveraging datasets from related domains, our approach enhances model generalization and robustness. Experimental results demonstrate substantial performance improvements over baseline methods, highlighting the potential of CDG in various industrial applications. The proposed methodology offers a scalable solution for supply chain quality control, enabling more reliable and efficient defect detection.

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Published

28-06-2024

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Section

Articles

How to Cite

Yli, S., & Choi, D. (2024). Enhancing Supply Chain Defect Detection through Cross-Domain Generalization: A Deep Learning Approach. Journal of Computing and Electronic Information Management, 13(2), 22-26. https://doi.org/10.54097/

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