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The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images.
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Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12873–12883 (June 2021)
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Rippel, O., Mertens, P., Merhof, D.: Modeling the distribution of normal data in pre-trained deep features for anomaly detection. ICPR (2020)
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Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recognition
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Božič, J., Tabernik, D., Skočaj, D.: Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Computers in Industry
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Shi, Y., Yang, J., Qi, Z.: Unsupervised anomaly segmentation via deep feature reconstruction. Neurocomputing
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Zavrtanik, V., Kristan, M., Skočaj, D.: Draem - a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8330–8339 (October 2021)
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Lei, L., Sun, S., Zhang, Y., Liu, H., Xu, W.: PSIC-Net: Pixel-wise segmentation and image-wise classification network for surface defects. Machines 2021
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