Takimoto, Hironori and Seki, Junya and F. Situju, Sulfayanti and Kanagawa, Akihiro (2022) Anomaly Detection Using Siamese Network with Attention Mechanism for Few-Shot Learning. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514
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Abstract
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.
Item Type: | Article |
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Subjects: | Souths Book > Computer Science |
Depositing User: | Unnamed user with email support@southsbook.com |
Date Deposited: | 17 Jun 2023 09:37 |
Last Modified: | 08 Jun 2024 09:08 |
URI: | http://research.europeanlibrarypress.com/id/eprint/1186 |