Methoden vergleichen

	Bayesian Autoencoder Anomaly Detection	Semi-supervised Autoencoder Anomalieerkennung
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2014–2015	2018–2020
Urheber	Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho	Ruff, L. et al.; Zong, B. et al.
Typ	Probabilistic generative model for unsupervised anomaly detection	Semi-supervised deep anomaly detection
Wegweisende Quelle	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).
Aliasnamen	Bayesian VAE anomaly detection, probabilistic autoencoder anomaly detection, variational autoencoder anomaly detection, VAE-based outlier detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Verwandt	5	5
Zusammenfassung	Bayesian Autoencoder Anomaly Detection uses a Variational Autoencoder — a probabilistic generative model trained on normal data — to flag anomalies by their high reconstruction error or low likelihood under the learned distribution. By treating the latent space as a probability distribution rather than a fixed point, it delivers principled uncertainty estimates alongside each anomaly score, making it especially valuable in high-stakes detection tasks.	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.