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Anomaly detection and localization in visual data, including images and videos, are crucial in machine learning and real-world applications.
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2020
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J. R. Kauffmann, L. Ruff, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller, “Toward explainable deep anomaly detection,” in
2020
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J. Tack, S. Mo, J. Jeong, and J. Shin, “Csi: Novelty detection via contrastive learning on distributionally shifted instances,”
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
2020
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S. Venkataramanan, K.-C. Peng, R. V. Singh, and A. Mahalanobis, “Attention guided anomaly localization in images,” in
2020
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D. Gudovskiy, S. Ishizaka, and K. Kozuka, “Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows,” in
2022
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2022
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2022
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2022
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Y.-H. Cao and J. Wu, “A random cnn sees objects: One inductive bias of cnn and its applications,” in
2022
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Y. Tang, L. Zhao, S. Zhang, C. Gong, G. Li, and J. Yang, “Integrating prediction and reconstruction for anomaly detection,”
2020
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G. Kwon, M. Prabhushankar, D. Temel, and G. AlRegib, “Backpropagated gradient representations for anomaly detection,” in
2020
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Y.-C. Hsu, Y. Shen, H. Jin, and Z. Kira, “Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,” in
2020
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Z. Xiao, Q. Yan, and Y. Amit, “Likelihood regret: An out-of-distribution detection score for variational auto-encoder,”
2020
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W. Liu, R. Li, M. Zheng, S. Karanam, Z. Wu, B. Bhanu, R. J. Radke, and O. Camps, “Towards visually explaining variational autoencoders,” in
2020
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G. Pang, C. Yan, C. Shen, A. v. d. Hengel, and X. Bai, “Self-trained deep ordinal regression for end-to-end video anomaly detection,” in
2020
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H. Park, J. Noh, and B. Ham, “Learning memory-guided normality for anomaly detection,” in
2020
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Z.-G. You, L. Cui, Y. Shen, W.-W. Liu, and T. Mei, “A unified model for multi-class anomaly detection,” in
2022
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2022
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2022
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2022
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2022
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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,”
2022
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A. Acsintoae, A. Florescu, M.-I. Georgescu, T. Mare, P. Sumedrea, R. T. Ionescu, F. S. Khan, and M. Shah, “Ubnormal: New benchmark for supervised open-set video anomaly detection,” in
2022
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E. Horwitz and Y. Hoshen, “Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection,” in
2023
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J. Liu, G. Xie, R. Chen, X. Li, J. Wang, Y. Liu, C. Wang, and F. Zheng, “Real3d-ad: A dataset of point cloud anomaly detection,”
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Z. Li, Y. Zhu, and M. Van Leeuwen, “A survey on explainable anomaly detection,”
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J. Jeong, Y. Zou, T. Kim, D. Zhang, A. Ravichandran, and O. Dabeer, “Winclip: Zero-/few-shot anomaly classification and segmentation,” in
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