Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching

Mohammad Zolfaghari, Hedieh Sajedi
12/20/2025
cs.CV

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Abstract

Anomaly detection or outlier is one of the challenging subjects in unsupervised learning . This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics . For this purpose , we first pre-train the ResNet-18 network on the ImageNet and then fine-tune it on the MVTech-AD dataset . Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods . Our model , Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.

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Cite this paper

@article{zolfaghari2025unsupervised,
  title  = {Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching},
  author = {Mohammad Zolfaghari and Hedieh Sajedi},
  year   = {2025},
  eprint = {2512.18219},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.18219v1}
}

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