Semi-supervised Defect Detection

Semi-Supervised Defect Detection is a defect detection framework that leverages both a small set of labeled defective samples and a large pool of unlabeled data to improve detection accuracy by exploiting underlying data structures and patterns through semi-supervised learning mechanisms.

76

Publications

3.9K

Citations

265

Authors

100

Institutions

Publications per year

2017–2026

56

Authors

265

  1. XJ
    Xiao‐Yuan Jing

    Wuhan University, China

    4 publications · 312 citations

    4

    h-index

  2. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    3 publications · 191 citations

    3

    h-index

  3. SM
    Siyamalan Manivannan

    University of Jaffna, Sri Lanka

    3 publications · 86 citations

    3

    h-index

  4. XL
    Xinyu Li

    Huazhong University of Science and Technology, China

    3 publications · 282 citations

    3

    h-index

  5. NS
    Naeem Seliya

    Florida Atlantic University, United States

    3 publications · 191 citations

    3

    h-index

1–5 of 265

Venues

  1. Sensors

    5 publications · 147 citations

    5

    h-index

  2. IEEE Access

    4 publications · 157 citations

    4

    h-index

  3. 3

    h-index

  4. IEEE Transactions on Industrial Informatics

    3 publications · 85 citations

    3

    h-index

  5. Measurement

    2 publications · 37 citations

    2

    h-index

1–5