Publisher
Springer Nature Switzerland
Reference72 articles.
1. An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1–18 (2015)
2. Auslander, B., Gupta, K.M., Aha, D.W.: A comparative evaluation of anomaly detection algorithms for maritime video surveillance. In: Carapezza, E.M. (ed.) Proceedings of the SPIE 8019, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X, p. 801907. SPIE Proceedings, SPIE (2011). https://doi.org/10.1117/12.883535
3. Berkhahn, F., Keys, R., Ouertani, W., Shetty, N., Geißler, D.: Augmenting variational autoencoders with sparse labels: a unified framework for unsupervised, semi-(un) supervised, and supervised learning. arXiv preprint arXiv:1908.03015 (2019)
4. Berns, F., Beecks, C.: Automatic Gaussian process model retrieval for big data. In: CIKM. ACM (2020)
5. Berns, F., Beecks, C.: Complexity-adaptive Gaussian process model inference for large-scale data. SIAM (2021). https://doi.org/10.1137/1.9781611976700.41