Linganisha mbinu

	Ugunduzi wa Anomali kwa Kutumia Autoencoder wa Kujifundisha Pekee	Variational Autoencoder
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2018–2020	2014
Mwanzilishi	Golan & El-Yaniv; broader self-supervised anomaly detection community	Kingma, D. P. & Welling, M.
Aina	Unsupervised / self-supervised deep learning	Deep generative latent-variable model (encoder–decoder)
Chanzo asilia	Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Majina mbadala	SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Zinazohusiana	6	5
Muhtasari	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.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.