Methoden vergleichen

	Semi-Supervised Learning	Variationaler Autoencoder
Fachgebiet	Maschinelles Lernen	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	1970s–2006 (formalized)	2014
Urheber	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Kingma, D. P. & Welling, M.
Typ	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Wegweisende Quelle	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).
Aliasnamen	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Verwandt	5	5
Zusammenfassung	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.