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
Parents
76
1–5 of 76
265
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h-index
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1–5 of 265
100
Wuhan University
Wuhan, China
5 h-index
11 publications
722 citations

Huazhong University of Science and Technology
Wuhan, China
5 h-index
19 publications
1.4K citations
Nanjing University of Posts and Telecommunications
Nanjing, China
4 h-index
16 publications
1.4K citations
Florida Atlantic University
Boca Raton, United States
3 h-index
4 publications
229 citations
University of Michigan–Dearborn
Dearborn, United States
3 h-index
3 publications
191 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
12 publications
255 citations
University of Jaffna
Jaffna, Sri Lanka
3 h-index
3 publications
86 citations
China University of Mining and Technology
Xuzhou, China
2 h-index
6 publications
150 citations
1–8 of 100
5 publications · 147 citations
5
h-index
4 publications · 157 citations
4
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3 publications · 98 citations
3
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3 publications · 85 citations
3
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2 publications · 37 citations
2
h-index
1–5