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Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples).
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2018
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2018
Cited alongside, same era.
2018
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2018
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2018
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2018
Cited alongside, same era.
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2018
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2018
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2020
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2020
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2021
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M.-I. Georgescu, A. Barbalau, R. T. Ionescu, F. S. Khan, M. Popescu, and M. Shah, “Anomaly detection in video via self-supervised and multi-task learning,” in CVPR , 2021, pp. 12 742–12 752
2021
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M. Salehi, N. Sadjadi, S. Baselizadeh, M. H. Rohban, and H. R. Rabiee, “Multiresolution knowledge distillation for anomaly detection,” in CVPR , 2021
2021
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2021
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2021
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T. Reiss, N. Cohen, L. Bergman, and Y. Hoshen, “Panda: Adapting pretrained features for anomaly detection and segmentation,” in CVPR , 2021, pp. 2806–2814
2021
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2021
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2021
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G. Pang, L. Cao, L. Chen, and H. Liu, “Learning representations of ultrahigh-dimensional data for random distance-based outlier detection,” in KDD , 2018, pp. 2041–2050
2050
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