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

• URL: http://arxiv.org/abs/2512.18219v1
• Date: Sat, 20 Dec 2025 05:22:55 GMT
• Title: Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching
• Authors: Mohammad Zolfaghari, Hedieh Sajedi,
• Abstract summary: This paper introduces a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics.<n>Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods.
• Score: 3.537921035534424
• License: http://creativecommons.org/licenses/by/4.0/
• 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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