Funder
Division of Civil, Mechanical and Manufacturing Innovation
Publisher
Springer Science and Business Media LLC
Reference79 articles.
1. An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 1–18.
2. Aparisi, F. (1996). Hotelling’s t2 control chart with adaptive sample sizes. International Journal of Production Research, 34(10), 2853–2862.
3. Batzner, K., Heckler, L., & König, R. (2023). Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv:2303.14535
4. Beheshti, I., Demirel, H., Initiative, A. D. N., et al. (2015). Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease. Computers in Biology and Medicine, 64, 208–216.
5. Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). Mvtec ad—A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp. 9592–9600).