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Bergmann, P., Fauser, M., Sattlegger, D. and Steger, C. (2019) Mvtec AD—A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 9584-9592.
https://doi.org/10.1109/cvpr.2019.00982

has been cited by the following article:

• TITLE: Unsupervised Anomaly Detection Algorithm Based on Bidirectional Knowledge Distillation Network

  AUTHORS: Hao Zhong, Shuai Kang, Ao Xiong

  KEYWORDS: Anomaly Detection, Knowledge Distillation, Unsupervised Learning

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.3, March 21, 2025

  ABSTRACT: Industrial appearance anomaly detection (AD) focuses on accurately identifying and locating abnormal regions in images. However, due to issues such as scarce abnormal samples, complex abnormal manifestations, and difficult abnormal annotation, the detection accuracy is limited. To solve these problems, based on the knowledge distillation framework, this paper proposes an unsupervised anomaly detection algorithm—Bidirectional knowledge distillation AD (BKD). This algorithm combines the advantages of forward and reverse distillation, enabling efficient anomaly detection. Experimental results have shown that the proposed method outperforms the state-of-the-art AD methods by 3% - 8% in AUC on the MVTec benchmarks.