Abstract
Defect detection has a wide range of applications in industry, and previous work has tended to be supervised learning, which typically requires a large number of samples. In this paper, we propose an unsupervised learning method that learns knowledge about normal images by distilling knowledge from a pre-trained expert network on ImageNet to a learner network of the same size. For a given input image, we use the differences in the features of the different layers of the expert network and learner network to detect and localize defects. We show that using comprehensive knowledge makes the differences between the two networks more apparent and that combining the differences in multi-level features can make the networks more generalizable. It's worth noting that we don't need to split the picture into patches to train, and we don't need to design the learner network additionally. Our general framework is relatively simple, yet has a good detection effect. We provide very competitive results on the MVTecAD dataset and DAGM dataset.
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Acknowledgements
This research was partially funded by Xianyang Science and Technology Research PlanProject (2021ZDYF-NY-O014) and Xi’an Science and Technology Plan Project (2022JH-JSYF-O270). All supports and assistance are sincerely appreciated
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Xie, H., Xiao, Y. (2023). Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection. In: Yu, C., Zhou, J., Song, X., Lu, Z. (eds) Green, Pervasive, and Cloud Computing. GPC 2022. Lecture Notes in Computer Science, vol 13744. Springer, Cham. https://doi.org/10.1007/978-3-031-26118-3_14
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