Multi-scale Feature Imitation for Unsupervised Anomaly Localization
- URL: http://arxiv.org/abs/2212.05786v2
- Date: Tue, 13 Dec 2022 02:40:13 GMT
- Title: Multi-scale Feature Imitation for Unsupervised Anomaly Localization
- Authors: Chao Hu, Shengxin Lai
- Abstract summary: A separate teacher-student feature imitation network structure and a multi-scale processing strategy are proposed to solve these problems.
The experimental results show that the proposed algorithm performs better than the feature modeling anomaly localization method on the real industrial product detection dataset.
- Score: 0.8122270502556375
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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