无监督异常检测模型的鲁棒性基准  

Robustness benchmark for unsupervised anomaly detection models

作　　者：王培 翟伟 曹洋[1,2] Pei Wang;Wei Zhai;Yang Cao(Department of Automation,University of Science and Technology of China,Hefei 230027,China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230088,China)

机构地区：[1]中国科学技术大学自动化系,安徽合肥230027 [2]合肥综合性国家科学中心人工智能研究院,安徽合肥230088

出　　处：《中国科学技术大学学报》2024年第1期20-29,19,I0001,I0002,共13页JUSTC

基　　金：supported by National Natural Science Foundation of China (62306295)。

摘　　要：由于生产环境的复杂性和多样性,了解无监督异常检测模型对常见降质的鲁棒性是至关重要的。为了系统地探索这个问题,我们提出一个名为MVTec-C的数据集来评估无监督异常检测模型的鲁棒性。基于这个数据集,我们探索了五种不同范式的方法的鲁棒性,包括基于重建的、基于表征相似度的、基于归一化流的、基于自监督表征学习的和基于知识蒸馏的范式。此外,我们还探讨了两种最佳的方法中不同模块对鲁棒性和准确性的影响,包括Patch Core方法中的多尺度特征、邻域大小、采样比例和Reverse Distillation方法中的多尺度特征、MMF模块与OCE模块、多尺度蒸馏。最后,我们提出了一个特征对齐模块(FAM),以减少降质带来的特征偏移,并将Patch Core和FAM结合起来,得到一个同时具备高准确率和高鲁棒性的模型。我们希望这项工作能够作为一种鲁棒性评估手段,并在将来为构建鲁棒的异常检测模型提供经验。Due to the complexity and diversity of production environments,it is essential to understand the robustness of unsupervised anomaly detection models to common corruptions.To explore this issue systematically,we propose a dataset named MVTec-C to evaluate the robustness of unsupervised anomaly detection models.Based on this dataset,we explore the robustness of approaches in five paradigms,namely,reconstruction-based,representation similarity-based,normalizing flow-based,self-supervised representation learning-based,and knowledge distillation-based paradigms.Furthermore,we explore the impact of different modules within two optimal methods on robustness and accuracy.This includes the multi-scale features,the neighborhood size,and the sampling ratio in the PatchCore method,as well as the multi-scale features,the MMF module,the OCE module,and the multi-scale distillation in the Reverse Distillation method.Finally,we propose a feature alignment module(FAM)to reduce the feature drift caused by corruptions and combine PatchCore and the FAM to obtain a model with both high performance and high accuracy.We hope this work will serve as an evaluation method and provide experience in building robust anomaly detection models in the future.

关 键 词：鲁棒性基准 异常检测 无监督学习 自动光学检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]