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• W3208797540 abstract "Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones." @default.
• W3208797540 created "2021-11-08" @default.
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• W3208797540 date "2021-03-06" @default.
• W3208797540 modified "2023-09-27" @default.
• W3208797540 title "Student-Teacher Feature Pyramid Matching for Anomaly Detection" @default.
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• W3208797540 doi "https://doi.org/10.48550/arxiv.2103.04257" @default.
• W3208797540 hasPublicationYear "2021" @default.
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