Data augmentation methods are commonly integrated into the training of
anomaly detection models. Previous approaches have primarily focused on
replicating real-world anomalies or enhancing diversity, without considering
that the standard of anomaly varies across different classes, potentially
leading to a biased training distribution.This paper analyzes crucial traits of
simulated anomalies that contribute to the training of reconstructive networks
and condenses them into several methods, thus creating a comprehensive
framework by selectively utilizing appropriate combinations.Furthermore, we
integrate this framework with a reconstruction-based approach and concurrently
propose a split training strategy that alleviates the issue of overfitting
while avoiding introducing interference to the reconstruction process. The
evaluations conducted on the MVTec anomaly detection dataset demonstrate that
our method outperforms the previous state-of-the-art approach, particularly in
terms of object classes.To evaluate generalizability, we generate a simulated
dataset comprising anomalies with diverse characteristics since the original
test samples only include specific types of anomalies and may lead to biased
evaluations. Experimental results demonstrate that our approach exhibits
promising potential for generalizing effectively to various unforeseen
anomalies encountered in real-world scenarios.

该论文分析了对训练重建网络起到贡献的模拟异常的关键特征，并基于这些特征提出了综合框架，结合重建方法和分离训练策略，解决了过拟合问题并避免干扰重建过程，评估结果表明该方法在对象类别方面优于现有方法，并证明其在真实场景下遇到各种意外异常具有良好的潜力。