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：数据增强方法通常被集成到异常检测模型的训练中。以往的研究主要聚焦于复制真实世界异常或增强多样性，却未考虑异常标准在不同类别间存在差异，这可能导致训练分布发生偏移。本文分析了有助于重建网络训练的模拟异常的关键特性，并将其提炼为多种方法，从而通过选择性运用适当组合构建了一个综合框架。此外，我们将该框架与基于重建的方法相结合，并同时提出一种分阶段训练策略，该策略在减轻过拟合问题的同时避免对重建过程引入干扰。在MVTec异常检测数据集上进行的评估表明，我们的方法优于先前的最先进方法，尤其在物体类别上表现突出。为评估泛化能力，我们生成了一个包含多种异常特征的模拟数据集——由于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，我们的方法有望对真实场景中各种未预见的异常实现有效泛化。