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    GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels

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    Date
    2022
    Author
    Jeon, Yongmoon
    Kim, Haneol
    Lee, Hyeona
    Jo, Seonghoon
    Kim, Jaewon
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    Abstract
    Image classification based on neural networks has been widely explored in machine learning and most research have focused on developing more efficient and accurate network models for given image dataset mostly over natural scene. However, industrial image data have different features with natural scene images in shape of target objects, background patterns, and color. Additionally, data imbalance is one of the most challenging problems to degrade classification accuracy for industrial images. This paper proposes a novel GAN-based image generation method to improve classification accuracy for defect images of OLED panels. We validate our method can synthetically generate defect images of OLED panels and classification accuracy can be improved by training minor classes with the generated defect images.
    BibTeX
    @inproceedings {10.2312:sr.20221164,
    booktitle = {Eurographics Symposium on Rendering},
    editor = {Ghosh, Abhijeet and Wei, Li-Yi},
    title = {{GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels}},
    author = {Jeon, Yongmoon and Kim, Haneol and Lee, Hyeona and Jo, Seonghoon and Kim, Jaewon},
    year = {2022},
    publisher = {The Eurographics Association},
    ISSN = {1727-3463},
    ISBN = {978-3-03868-187-8},
    DOI = {10.2312/sr.20221164}
    }
    URI
    https://doi.org/10.2312/sr.20221164
    https://diglib.eg.org:443/handle/10.2312/sr20221164
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA