Anomaly Detection Using Siamese Network with Attention Mechanism for Few-Shot Learning
Hironori Takimoto, Junya Seki, Sulfayanti F. Situju, Akihiro Kanagawa
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.
Topics & Concepts
Computer scienceMechanism (biology)Artificial intelligenceAnomaly detectionTask (project management)Deep learningAttention networkOne shotMachine learningVisual attentionManagementPerceptionEconomicsNeuroscienceMechanical engineeringBiologyEngineeringPhilosophyEpistemologyAnomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect DetectionAdversarial Robustness in Machine Learning