Comparer des méthodes

	Modèle de Mélange Gaussien Auto-supervisé	Apprentissage semi-supervisé	Autoencodeur Variationnel
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage profond
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2010s–2019	1970s–2006 (formalized)	2014
Auteur d'origine	Multiple authors (Zhai et al., 2019; earlier formulations in semi-supervised GMM literature)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Kingma, D. P. & Welling, M.
Type	Probabilistic generative model with self-supervised pretraining	Learning paradigm	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	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	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Apparentées	2	5	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	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.