Methoden vergelijken

	Active Learning Autoencoder Anomaly Detection	Bayesiaanse Autoencoder Anomaliedetectie
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2014–2018	2014–2015
Grondlegger	Multiple (Guo et al.; Pimentel et al.)	Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho
Type	Active learning + unsupervised deep anomaly detection hybrid	Probabilistic generative model for unsupervised anomaly detection
Oorspronkelijke bron	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).
Aliassen	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	Bayesian VAE anomaly detection, probabilistic autoencoder anomaly detection, variational autoencoder anomaly detection, VAE-based outlier detection
Verwant	6	5
Samenvatting	Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.	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.