Unsupervised Defect Detection

Unsupervised Defect Detection is a defect detection paradigm that belongs to the class of unsupervised learning methods, distinguished by its ability to identify anomalies or defects in data without requiring labeled examples of defective instances.

97

Publications

6.9K

Citations

326

Authors

140

Institutions

Publications per year

2017–2026

82

Authors

326

  1. SL
    Shuai Lu

    Beijing Institute of Technology, China

    4 publications · 118 citations

    4

    h-index

  2. CS
    Carsten Steger

    Software (Germany), Germany

    4 publications · 3.1K citations

    4

    h-index

  3. PB
    Paul Bergmann

    Software (Germany), Germany

    4 publications · 3.1K citations

    4

    h-index

  4. MF
    Michael Fauser

    Software (Germany), Germany

    4 publications · 3.1K citations

    4

    h-index

  5. DS
    David Sattlegger

    Technical University of Munich, Germany

    4 publications · 3.1K citations

    4

    h-index

1–5 of 326

Venues

  1. 8

    h-index

  2. IEEE Access

    6 publications · 256 citations

    6

    h-index

  3. IEEE Transactions on Industrial Informatics

    5 publications · 265 citations

    6

    h-index

  4. Textile Research Journal

    4 publications · 115 citations

    4

    h-index

  5. Sensors

    3 publications · 67 citations

    4

    h-index

1–5