Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation

• URL: http://arxiv.org/abs/2311.14506v2
• Date: Fri, 2 Feb 2024 12:48:45 GMT
• Title: Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
• Authors: Mehdi Rafiei, Alexandros Iosifidis
• Abstract summary: This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process. The proposed Regularized Discriminative Coupled-hypersphere-based Feature Adaptation (RD-CFA) forms a solution for multi-class anomaly detection.
• Score: 85.15324009378344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In anomaly detection, identification of anomalies across diverse product categories is a complex task. This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process of Coupled-hypersphere-based Feature Adaptation (CFA). By doing so, the proposed Regularized Discriminative Coupled-hypersphere-based Feature Adaptation (RD-CFA), forms a solution for multi-class anomaly detection. By using the discriminative power of RD-VAE to capture intricate class distributions, combined with CFA's robust anomaly detection capability, the proposed method excels in discerning anomalies across various classes. Extensive evaluations on multi-class anomaly detection and localization using the MVTec AD and BeanTech AD datasets showcase the effectiveness of RD-CFA compared to eight leading contemporary methods.

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