Comparer des méthodes

	Détection d'anomalies par autoencodeur en ensemble	Détection d'anomalies par auto-encodeur semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2018–2020
Auteur d'origine	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.	Ruff, L. et al.; Zong, B. et al.
Type	Ensemble unsupervised anomaly detection	Semi-supervised deep anomaly detection
Source fondatrice	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM.	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	ensemble AE anomaly detection, autoencoder ensemble outlier detection, multi-autoencoder anomaly scoring, AE ensemble unsupervised anomaly detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Apparentées	5	5
Résumé	Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices.	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.