विधियों की तुलना करें

	स्व-पर्यवेक्षित ऑटोएन्कोडर विसंगति संसूचन	आइसोलेशन फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2018–2020	2008
प्रवर्तक	Golan & El-Yaniv; broader self-supervised anomaly detection community	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
प्रकार	Unsupervised / self-supervised deep learning	Unsupervised ensemble (random partitioning trees)
मौलिक स्रोत	Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
उपनाम	SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
संबंधित	6	5
सारांश	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.