مقایسهٔ روش‌ها

	یادگیری خودنظارتی ماشین بردار تک کلاسه (Self-supervised One-class SVM)	تشخیص ناهنجاری با خودرمزگذار
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2018	2006–2014
پدیدآور	Golan & El-Yaniv; Ruff et al.	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
نوع	Self-supervised anomaly/novelty detection	Unsupervised deep learning (reconstruction-based)
منبع بنیادین	Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.	Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
نام‌های دیگر	SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection
مرتبط	6	3
خلاصه	Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.	Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.