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Anomaly detection has a wide range of applications and is especially important in industrial quality inspection.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13
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Akçay, S., Atapour-Abarghouei, A., Breckon, T.P.: Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In: 2019 International Joint Conference on Neural Networks (IJCNN). pp. 1–8 (2019). https://doi.org/10.1109/IJCNN.2019.8851808
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Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9584–9592 (2019). https://doi.org/10.1109/CVPR.2019.00982
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Shi, Y., Yang, J., Qi, Z.: Unsupervised anomaly segmentation via deep feature reconstruction. Neurocomputing 424
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Tang, T.W., Kuo, W.H., Lan, J.H., Ding, C.F., Hsu, H., Young, H.T.: Anomaly detection neural network with dual auto-encoders gan and its industrial inspection applications. Sensors 20
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Gudovskiy, D., Ishizaka, S., Kozuka, K.: Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 1819–1828 (2022). https://doi.org/10.1109/WACV51458.2022.00188
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Jiang, J., Zhu, J., Bilal, M., Cui, Y., Kumar, N., Dou, R., Su, F., Xu, X.: Masked swin transformer unet for industrial anomaly detection. IEEE Transactions on Industrial Informatics 19
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Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: A patch distribution modeling framework for anomaly detection and localization. In: Pattern Recognition. ICPR International Workshops and Challenges. pp. 475–489. Springer International Publishing, Cham (2021)
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Jezek, S., Jonak, M., Burget, R., Dvorak, P., Skotak, M.: Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. In: 2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). pp. 66–71 (2021). https://doi.org/10.1109/ICUMT54235.2021.9631567
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Zavrtanik, V., Kristan, M., Skočaj, D.: DrÆm – a discriminatively trained reconstruction embedding for surface anomaly detection. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8310–8319 (2021). https://doi.org/10.1109/ICCV48922.2021.00822
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Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9727–9736 (2022). https://doi.org/10.1109/CVPR52688.2022.00951
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Fan, C.M., Liu, T.J., Liu, K.H., Chiu, C.H.: Selective residual m-net for real image denoising. In: 2022 30th European Signal Processing Conference (EUSIPCO). pp. 469–473 (2022). https://doi.org/10.23919/EUSIPCO55093.2022.9909521
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Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14298–14308 (2022). https://doi.org/10.1109/CVPR52688.2022.01392
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Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Fully convolutional cross-scale-flows for image-based defect detection. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 1829–1838 (2022). https://doi.org/10.1109/WACV51458.2022.00189
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Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., Wu, L.: Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. In: 2022 IEEE International Conference on Multimedia and Expo (ICME). pp. 1–6 (2022). https://doi.org/10.1109/ICME52920.2022.9859925
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Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: Computer Vision – ECCV 2022. pp. 392–408. Springer Nature Switzerland, Cham (2022)
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