Compara mètodes

	Aprenentatge semi-supervisat	Variational Autoencoder
Camp	Aprenentatge automàtic	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	1970s–2006 (formalized)	2014
Autor original	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Kingma, D. P. & Welling, M.
Tipus	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Font seminal	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).
Àlies	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Relacionats	5	5
Resum	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.