论文标题
基准测试无监督的异常检测和定位
Benchmarking Unsupervised Anomaly Detection and Localization
论文作者
论文摘要
在计算机视觉中，无监督的异常检测和本地化是最实际，最具挑战性的问题之一，近年来受到了极大的关注。从提出MVTEC AD数据集的时间到当前，不断提出的新研究方法将其精度推向饱和。现在是时候对现有方法进行全面比较以激发进一步的研究。本文从无监督的异常检测和本地化任务中的性能方面进行了广泛的比较，并添加了社区以前忽略的推理效率的比较。同时，还提供了对MVTEC AD数据集的分析，尤其是影响该模型的标签歧义无法实现完整的标记。此外，考虑到新的MVTEC 3D-AD数据集的建议，本文还使用此新数据集中的现有最新2D方法进行了实验，并通过分析报告了相应的结果。
Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehensive comparison of existing methods to inspire further research. This paper extensively compares 13 papers in terms of the performance in unsupervised anomaly detection and localization tasks, and adds a comparison of inference efficiency previously ignored by the community. Meanwhile, analysis of the MVTec AD dataset are also given, especially the label ambiguity that affects the model fails to achieve full marks. Moreover, considering the proposal of the new MVTec 3D-AD dataset, this paper also conducts experiments using the existing state-of-the-art 2D methods on this new dataset, and reports the corresponding results with analysis.
