Abstract
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.
Abstract (translated)
基于重构的异常检测模型通过抑制异常泛化能力来实现其目的,但不同正常模式的重构结果并不良好。尽管已经通过建模样本多样性来缓解这个问题,但由于不希望传输异常信息而导致了快速学习。在本文中,为了更好地处理权衡问题,我们提出了多样性可测量的异常检测框架(DMAD),以增强重构多样性,同时避免对异常的不希望泛化。为此,我们设计了一个金字塔变形模块(PDM),该模块将不同的正常模式建模,并通过估计从重构参考到原始输入的多尺度变形场来估计异常的严重性。与信息压缩模块集成在一起,PDM实际上将变形与原型嵌入分离,从而使最终的异常得分更加可靠。在监控视频和工业图像的实验结果中,证明了我们的方法和DMAD的有效性。此外,DMAD在污染数据和类似异常的正常样本中同样有效。
URL
https://arxiv.org/abs/2303.05047
