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Yang, Junyang; Cao, Jiuxin; Duan, Chengge (2025) Towards Realistic Industrial Anomaly Detection: MADE-Net Framework and ManuDefect-21 Benchmark. Applied Sciences, 15 (20). doi:10.3390/app152010885

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Reference TypeJournal (article/letter/editorial)
TitleTowards Realistic Industrial Anomaly Detection: MADE-Net Framework and ManuDefect-21 Benchmark
JournalApplied Sciences
AuthorsYang, JunyangAuthor
Cao, JiuxinAuthor
Duan, ChenggeAuthor
Year2025 (October 10)Volume15
Issue20
PublisherMDPI AG
DOIdoi:10.3390/app152010885Search in ResearchGate
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Mindat Ref. ID19050656Long-form Identifiermindat:1:5:19050656:0
GUID0
Full ReferenceYang, Junyang; Cao, Jiuxin; Duan, Chengge (2025) Towards Realistic Industrial Anomaly Detection: MADE-Net Framework and ManuDefect-21 Benchmark. Applied Sciences, 15 (20). doi:10.3390/app152010885
Plain TextYang, Junyang; Cao, Jiuxin; Duan, Chengge (2025) Towards Realistic Industrial Anomaly Detection: MADE-Net Framework and ManuDefect-21 Benchmark. Applied Sciences, 15 (20). doi:10.3390/app152010885
In(2025, October) Applied Sciences Vol. 15 (20). MDPI AG

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Qiang, Y., Cao, J., Zhou, S., Yang, J., Yu, L., and Liu, B. (2025). tGARD: Text-Guided Adversarial Reconstruction for Industrial Anomaly Detection. IEEE Trans. Ind. Inform., 1–12.
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