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.

翻译：数据增强方法常被集成至异常检测模型的训练中。以往方法主要聚焦于复制真实异常或增强多样性，却未考虑不同类别间的异常标准存在差异，这可能导致训练分布出现偏差。本文分析了模拟异常中能促进重建网络训练的关键特征，并将其凝练为若干方法，通过选择性运用合适组合构建出综合框架。进一步地，我们将此框架与基于重建的方法相集成，并提出一种分段训练策略，该策略在避免干扰重建过程的同时缓解过拟合问题。在MVTec异常检测数据集上的评估表明，我们的方法在物体类别上显著优于先前的最优方法。为评估泛化能力，我们生成了一个包含多样化异常特征的模拟数据集（因原始测试样本仅包含特定类型异常，可能导致评估偏差）。实验结果表明，我们的方法在泛化至真实场景中各类未知异常方面展现出良好潜力。