Compară metode

	Detecția anomaliilor cu autoencoder auto-supervizat	SVM pentru o singură clasă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2018–2020	1999–2001
Autorul original	Golan & El-Yaniv; broader self-supervised anomaly detection community	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Tip	Unsupervised / self-supervised deep learning	Anomaly / novelty detection (unsupervised)
Sursa seminală	Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
Denumiri alternative	SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Înrudite	6	3
Rezumat	Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.