Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

翻译：基于重构的异常检测模型通过抑制对异常的泛化能力来实现其目标。然而，多样化的正常模式也因此无法得到良好重构。尽管已有研究尝试通过建模样本多样性来缓解这一问题，但由于异常信息的不必要传递，这些方法常遭受捷径学习的影响。为更好地处理这一权衡问题，本文提出可度量多样性的异常检测（DMAD）框架，旨在增强重构多样性的同时避免对异常的意外泛化。为此，我们设计金字塔形变模块（PDM），通过估计从重构参考到原始输入的多尺度形变场，建模多样化的正常模式并度量异常的严重程度。结合信息压缩模块，PDM本质上将形变从原型嵌入中解耦，使最终的异常分数更为可靠。在监控视频和工业图像上的实验结果均证明了我们方法的有效性。此外，DMAD在面对污染数据和类异常正常样本时同样表现优异。