Comparer des méthodes

	Modèle de Mélange Gaussien Auto-supervisé	Autoencodeur Variationnel
Domaine	Apprentissage automatique	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s–2019	2014
Auteur d'origine	Multiple authors (Zhai et al., 2019; earlier formulations in semi-supervised GMM literature)	Kingma, D. P. & Welling, M.
Type	Probabilistic generative model with self-supervised pretraining	Deep generative latent-variable model (encoder–decoder)
Source fondatrice	Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4L: Self-supervised semi-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1476–1485.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alias	SS-GMM, self-supervised GMM, semi-supervised Gaussian mixture model, self-supervised clustering with GMM	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Apparentées	2	5
Résumé	A Self-supervised Gaussian Mixture Model (SS-GMM) combines self-supervised representation learning with a probabilistic Gaussian mixture prior to discover meaningful clusters in unlabeled or partially labeled data. By leveraging pretext tasks to learn rich embeddings before fitting a GMM, it achieves cluster quality that standard GMMs applied to raw features rarely reach, especially on complex image, text, or biological data.	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.