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.

通过修改的正则化判别变分自动编码器（RD-VAE）在耦合超球特征自适应（CFA）的特征提取过程中引入了类鉴别特性，提出了一种新的模型 RD-CFA，用于多类别异常检测，该方法综合了 RD-VAE 捕捉复杂类别分布的辨别能力与 CFA 强大的异常检测能力，相比于八种主流方法，在多类别异常检测和定位上使用 MVTec AD 和 BeanTech AD 数据集进行广泛评估，显示出了 RD-CFA 方法的有效性。