قارن الطرق

	الكشف عن الشذوذ باستخدام التشفير التلقائي ذاتي الإشراف	آلة المتجهات الداعمة أحادية الفئة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2018–2020	1999–2001
صاحب الطريقة	Golan & El-Yaniv; broader self-supervised anomaly detection community	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
النوع	Unsupervised / self-supervised deep learning	Anomaly / novelty detection (unsupervised)
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	6	3
الملخص	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.