Unsupervised selective labeling for semi-supervised industrial defect detection

In industrial detection scenarios, achieving high accuracy typically relies on extensive labeled datasets, which are costly and time-consuming. This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce anno...

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Bibliographic Details

Published in:	Journal of King Saud University. Computer and information sciences Vol. 36; no. 8; p. 102179
Main Authors:	Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen
Format:	Journal Article
Language:	English
Published:	Springer 01.10.2024
Subjects:	Industrial defect detection Semi-supervised learning Unsupervised spectral clustering labeling
ISSN:	1319-1578
Online Access:	Get full text
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