Uporedite metode

	Semi-supervised Learning	Varijacioni autoenkoder
Oblast	Mašinsko učenje	Duboko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	1970s–2006 (formalized)	2014
Tvorac	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Kingma, D. P. & Welling, M.
Tip	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Temeljni izvor	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).
Drugi nazivi	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Srodne	5	5
Sažetak	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.