[51] Shinji Yamada and Kazuhiro Hotta. Reconstruction student with attention for student-teacher pyramid matching. arXiv preprint arXiv:2111.15376, 2021.
[52] Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Heng. Learning semantic context from normal samples for unsupervised anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 3110–3118, 2021.
[53] Yi Yan, Deming Wang, Guangliang Zhou, and Qijun Chen. Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition. IEEE Transactions on Instrumentation and Measurement, 70:1-12, 2021.
[54] Minghui Yang, Peng Wu, and Hui Feng. Memseg: A semi-supervised method for image surface defect detection using differences and commonalities. Engineering Applications of Artificial Intelligence, 119:105835, 2023.
[55] Lin Yen-Chen, Pete Florence, Jonathan T Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin. inerf: Inverting neural radiance fields for pose estimation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1323-1330. IEEE, 2021.
[56] Jihun Yi and Sungroh Yoon. Patch svdd: Patch-level svdd for anomaly detection and segmentation. In Proceedings of the Asian Conference on Computer Vision, 2020.
[57] Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le. A unified model for multi-class anomaly detection. arXiv preprint arXiv:2206.03687, 2022.
[58] Zhiyuan You, Kai Yang, Wenhan Luo, Lei Cui, Yu Zheng, and Xinyi Le. Adtr: Anomaly detection transformer with feature reconstruction. arXiv preprint arXiv:2209.01816, 2022.
[59] Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, and Liwei Wu. Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677, 2021.
[60] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8330-8339, 2021.
[61] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. Reconstruction by inpainting for visual anomaly detection. Pattern Recognition, 112:107706, 2021.
[62] Zheng Zhang and Xiaogang Deng. Anomaly detection using improved deep svdd model with data structure preservation. Pattern Recognition Letters, 148:1-6, 2021.
[63] Zhenxin Zhu, Yuantao Chen, Zirui Wu, Chao Hou, Yongliang Shi, Chuxuan Li, Pengfei Li, Hao Zhao, and Guyue Zhou. Latitude: Robotic global localization with truncated dynamic low-pass filter in city-scale nerf. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 8326-8332. IEEE, 2023.
[64] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys. Nice-slam: Neural implicit scalable encoding for slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12786-12796, 2022.
[65] Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer. Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In European Conference on Computer Vision, pages 392-408. Springer, 2022.