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Multi-scale Feature Imitation for Unsupervised Anomaly Localization

arXiv 2212.05786 ↗First posted 12 Dec 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

The unsupervised anomaly localization task faces the challenge of missing anomaly sample training, detecting multiple types of anomalies, and dealing with the proportion of the area of multiple anomalies. A separate teacher-student feature imitation network structure and a multi-scale processing strategy combining an image and feature pyramid are proposed to solve these problems. A network module importance search method based on gradient descent optimization is proposed to simplify the network structure. The experimental results show that the proposed algorithm performs better than the feature modeling anomaly localization method on the real industrial product detection dataset in the same period. The multi-scale strategy can effectively improve the effect compared with the benchmark method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 12 Dec 2022.

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References · 15

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  11. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  12. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
3 more
  1. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  2. 2018Learning Deep Features for One-Class Classificationcited by 76
  3. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71

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