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In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed.
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J. Yue-Hei Ng, F. Yang, and L. S. Davis, “Exploiting local features from deep networks for image retrieval,” in IEEE Conf. Comput. Vis. Pattern Recog. Worksh. , 2015, pp. 53–61
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Y. Kalantidis, C. Mellina, and S. Osindero, “Cross-dimensional weighting for aggregated deep convolutional features,” in Eur. Conf. Comput. Vis. Worksh. Springer, 2016, pp. 685–701
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2018
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J. Xu, C. Shi, C. Qi, C. Wang, and B. Xiao, “Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval,” in AAAI . AAAI Press, 2018, pp. 7436–7443
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P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Mvtec AD - A comprehensive real-world dataset for unsupervised anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. Computer Vision Foundation / IEEE, 2019, pp. 9592–9600
2019
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M. Teichmann, A. Araujo, M. Zhu, and J. Sim, “Detect-to-retrieve: Efficient regional aggregation for image search,” in IEEE Conf. Comput. Vis. Pattern Recog. Computer Vision Foundation / IEEE, 2019, pp. 5109–5118
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P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Mvtec AD - A comprehensive real-world dataset for unsupervised anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. Computer Vision Foundation / IEEE, 2019, pp. 9592–9600
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Int. Conf. Learn. Represent. OpenReview.net, 2019
2019
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M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in AAAI Conference on Artificial Intelligence , ser. Proceedings of Machine Learning Research, vol. 97. PMLR, 2019, pp. 6105–6114
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Adv. Neural Inform. Process. Syst. , 2019, pp. 8024–8035
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J. Yi and S. Yoon, “Patch svdd: Patch-level svdd for anomaly detection and segmentation,” in ACCV , 2020
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P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, and C. Steger, “Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization,” Int. J. Comput. Vis. , vol. 130, no. 4, p. 947–969, 2022
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2020
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P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings,” in IEEE Conf. Comput. Vis. Pattern Recog. IEEE, 2020, pp. 4182–4191
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P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, M. Kloft, and K. R. Muller, “Explainable deep one-class classification,” in Int. Conf. Learn. Represent. , 2021
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T. Defard, A. Setkov, A. Loesch, and R. Audigier, “Padim: A patch distribution modeling framework for anomaly detection and localization,” in Pattern Recognition. ICPR International Workshops and Challenges . Cham: Springer International Publishing, 2021, pp. 475–489
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2021
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C.-L. Li, K. Sohn, J. Yoon, and T. Pfister, “Cutpaste: Self-supervised learning for anomaly detection and localization,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021, pp. 9659–9669
2021
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V. Zavrtanik, M. Kristan, and D. Skočaj, “DrÆm – a discriminatively trained reconstruction embedding for surface anomaly detection,” in Int. Conf. Comput. Vis. , 2021, pp. 8310–8319
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M. Salehi, N. Sadjadi, S. Baselizadeh, M. H. Rohban, and H. R. Rabiee, “Multiresolution knowledge distillation for anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021, pp. 14 902–14 912
2021
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R. Saiku, J. Sato, T. Yamada, and K. Ito, “Enhancing anomaly detection performance and acceleration,” IEEJ Journal of Industry Applications , vol. 11, no. 4, pp. 616–622, 2022
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C. Huang, H. Guan, A. Jiang, Y. Zhang, M. Spratling, and Y.-F. Wang, “Registration based few-shot anomaly detection,” in Eur. Conf. Comput. Vis. Cham: Springer Nature Switzerland, 2022, pp. 303–319
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Y. Zheng, X. Wang, R. Deng, T. Bao, R. Zhao, and L. Wu, “Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization,” in Int. Conf. Multimedia and Expo . IEEE, 2022, pp. 1–6
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C. Ding, G. Pang, and C. Shen, “Catching both gray and black swans: Open-set supervised anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 7378–7388
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N.-C. Ristea, N. Madan, R. T. Ionescu, K. Nasrollahi, F. S. Khan, T. B. Moeslund, and M. Shah, “Self-supervised predictive convolutional attentive block for anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 13 566–13 576
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J. Lei, X. Hu, Y. Wang, and D. Liu, “Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 14 143–14 152
2023
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M. Yang, P. Wu, and H. Feng, “Memseg: A semi-supervised method for image surface defect detection using differences and commonalities,” Engineering Applications of Artificial Intelligence , vol. 119, p. 105835, 2023
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X. Zhang, S. Li, X. Li, P. Huang, J. Shan, and T. Chen, “Destseg: Segmentation guided denoising student-teacher for anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 3914–3923
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W. Liu, H. Chang, B. Ma, S. Shan, and X. Chen, “Diversity-measurable anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 12 147–12 156
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W. Chen, Y. Liu, W. Wang, E. M. Bakker, T. Georgiou, P. Fieguth, L. Liu, and M. S. Lew, “Deep learning for instance retrieval: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 6, p. 7270–7292, 2023
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H. Zhang, Z. Wu, Z. Wang, Z. Chen, and Y.-G. Jiang, “Prototypical residual networks for anomaly detection and localization,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 16 281–16 291
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X. Yao, R. Li, J. Zhang, J. Sun, and C. Zhang, “Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 24 490–24 499
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Y. Cao, X. Xu, Z. Liu, and W. Shen, “Collaborative discrepancy optimization for reliable image anomaly localization,” IEEE Transactions on Industrial Informatics , pp. 1–10, 2023
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Z. Liu, Y. Zhou, Y. Xu, and Z. Wang, “Simplenet: A simple network for image anomaly detection and localization,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2023, pp. 20 402–20 411
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M. Rudolph, T. Wehrbein, B. Rosenhahn, and B. Wandt, “Asymmetric student-teacher networks for industrial anomaly detection,” in IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 2592–2602
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K. Batzner, L. Heckler, and R. König, “Efficientad: Accurate visual anomaly detection at millisecond-level latencies,” 2023
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