Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
This work addresses anomaly detection across diverse product categories, presenting an incremental improvement by combining existing techniques.
The paper tackled multi-class anomaly detection by integrating a modified Regularized Discriminative Variational Auto-Encoder into Coupled-hypersphere-based Feature Adaptation, achieving improved performance over eight leading methods on MVTec AD and BeanTech AD datasets.
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.