Anomaly Detection Using Siamese Network with Attention Mechanism for Few-Shot Learning

Automated inspection using deep-learning has been attracting attention for visual inspection at the manufacturing site. However, the inability to obtain sufficient abnormal product data for training deep- learning models is a problem in practical application. This study proposes an anomaly detection...

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Bibliographic Details
Published in:Applied artificial intelligence 2022-12, Vol.36 (1)
Main Authors: Takimoto, Hironori, Seki, Junya, F. Situju, Sulfayanti, Kanagawa, Akihiro
Format: Article
Language:English
Subjects:
Anomalies
Artificial neural networks
Deep learning
Inspection
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Summary:Automated inspection using deep-learning has been attracting attention for visual inspection at the manufacturing site. However, the inability to obtain sufficient abnormal product data for training deep- learning models is a problem in practical application. This study proposes an anomaly detection method based on the Siamese network with an attention mechanism for a small dataset. Moreover, attention branch loss (ABL) is proposed for Siamese network to render more task-specific attention maps from attention mechanism. Experimental results confirm that the proposed method with the attention mechanism and ABL is effective even with limited abnormal data.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2022.2094885