Linganisha mbinu

	Uchunguzi wa Hitilafu kwa Kutumia Kujifunza Amilifu na Kirejeshi Kiotomatiki	Uchambuzi wa Anomali kwa Kutumia Bayesian Autoencoder
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2018	2014–2015
Mwanzilishi	Multiple (Guo et al.; Pimentel et al.)	Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho
Aina	Active learning + unsupervised deep anomaly detection hybrid	Probabilistic generative model for unsupervised anomaly detection
Chanzo asilia	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).
Majina mbadala	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
Zinazohusiana	6	5
Muhtasari	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.