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Intra-class Structure Aware Networks for Screen Defect Detection

  • Chengchao Shen
  • Jie Song
  • Shuyi Song
  • Sihui Luo
  • Li Sun
  • Mingli SongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

Typically, screen defect detection treats different types of defects as a single category and ignores the large variation among them, which may pose large difficulty to the model learning and thus lead to inferior performance. In this paper, we propose a novel network model, called Intra-class Structure Aware Networks (ISANs), to alleviate the difficulty of learning one single concept which exhibits in various forms. The proposed model introduces more neural units for the “defect” category rather than a single one, to accommodate the large variations in this category, which can significantly improve the representation power. Regularized by prior distribution of intra-class variants, our approach can learn intra-class structure of screen defect without extra fine-grained labels. Experimental results demonstrate that ISANs can effectively discriminate intra-class variants and gain significant performance improvement on screen defect detection task as well as the classification task in MNIST.

Keywords

Intra-class Structure Aware Convolutional Neural Networks Screen defect detection 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (61572428,U1509206), Fundamental Research Funds for the Central Universities (2017FZA5014), National Key Research and Development Program (2016YFB1200203) and Key Research and Development Program of Zhejiang Province (2018C01004).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chengchao Shen
    • 1
  • Jie Song
    • 1
  • Shuyi Song
    • 1
  • Sihui Luo
    • 1
  • Li Sun
    • 1
  • Mingli Song
    • 1
    Email author
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina

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