Comparar métodos

	Detección de Anomalías con Autoencoder y Aprendizaje Activo	Detección de anomalías con autoencoder semi-supervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2014–2018	2018–2020
Autor original	Multiple (Guo et al.; Pimentel et al.)	Ruff, L. et al.; Zong, B. et al.
Tipo	Active learning + unsupervised deep anomaly detection hybrid	Semi-supervised deep anomaly detection
Fuente seminal	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	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).
Alias	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Relacionados	6	5
Resumen	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.	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.