Comparer des méthodes

	Détection d'anomalies par autoencodeur robuste	SVM à une classe robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2000s–2010s
Auteur d'origine	Zhou, C. & Paffenroth, R. C.	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s
Type	Unsupervised anomaly detection (robust deep learning)	Anomaly detection / novelty detection
Source fondatrice	Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.
Alias	Robust Deep Autoencoder, Robust AE Anomaly Detection, RDAE, Robust Reconstruction-Based Anomaly Detection	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM
Apparentées	5	5
Résumé	Robust Autoencoder Anomaly Detection extends the standard autoencoder framework with robustness mechanisms — such as sparse decomposition, robust loss functions, or adversarial regularisation — so that the model learns a compact representation of normal behaviour while remaining resistant to the corrupting influence of anomalies embedded in the training data.	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.