Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates multiple counterfactual examples for each anomaly, capturing diverse concepts of anomalousness. A counterfactual example is a modification of the anomaly that is perceived as normal by the anomaly detector. The method provides a high-level semantic explanation of the mechanism that triggered the anomaly detector, allowing users to explore "what-if scenarios." Qualitative and quantitative analyses across various image datasets show that the method applied to state-of-the-art anomaly detectors can achieve high-quality semantic explanations of detectors.

翻译：基于深度学习的方法在图像异常检测领域取得了突破性进展，但其复杂性为理解为何某个实例被预测为异常带来了巨大挑战。我们提出了一种新颖的解释方法，该方法为每个异常生成多个反事实示例，捕捉异常性的多样化概念。反事实示例是对异常的修改，使其被异常检测器判定为正常。该方法提供了触发异常检测器机制的高层语义解释，使用户能够探索"假设场景"。跨多种图像数据集的定性与定量分析表明，将该方法应用于最先进的异常检测器，能够生成检测器的高质量语义解释。