Comparer des méthodes

	Isolation Forest auto-supervisé	Autoencodeur
Domaine	Apprentissage automatique	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2008–2020s	2006
Auteur d'origine	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Hinton, G.E. & Salakhutdinov, R.R.
Type	Ensemble anomaly detector with self-supervised pre-training	Neural network (encoder-decoder)
Source fondatrice	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.
Alias	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network
Apparentées	4	4
Résumé	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.