Study on Anomaly Classifier with Domain Adaptation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55976-1_1
Reference11 articles.
1. Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., van den Hengel, A.: Memorizing normality to detect anomaly: Memory-augmented deep auto encoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705–1714 (2019)
2. Ristea, N.-C., Madan, N., Ionescu, R.T., Nasrollahi, K., Shahbaz Khan, F., Moeslund, T.B., Shah, M.: Self-supervised predictive convolutional attentive block for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13576–13586 (2022)
3. Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664–9674 (2021)
4. Zavrtanik, V., Kristan, M., Skocaj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330–8339 (2021)
5. Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., et al. (eds.) Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part IV, pp. 475–489. Springer International Publishing, Cham (2021)
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