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：在异常检测模型的训练中，数据增强方法通常被整合进来。以往的方法主要关注于复制真实世界中的异常或增强多样性，而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产生偏差。本文分析了有助于重建网络训练的模拟异常的关键特性，并将其归纳为若干方法，从而通过选择性使用合适的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，并同时提出了一种分割训练策略，该策略在缓解过拟合问题的同时避免干扰重建过程。在MVTec异常检测数据集上的评估表明，我们的方法优于先前的最先进方法，尤其是在对象类别方面。为了评估泛化能力，我们生成了一个包含多种异常特征的模拟数据集——因为原始测试样本仅包含特定类型的异常，可能导致评估偏差。实验结果表明，我们的方法在有效泛化至真实场景中各种未见异常方面展现出良好的潜力。