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	Wariacyjny autoenkoder półnadzorowany	Uczenie transferowe z wariacyjnym autoenkoderem
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2014	2014 (VAE); 2010 (transfer learning survey)
Twórca	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang
Typ	Generative probabilistic model (semi-supervised)	Generative model with transferred encoder/decoder
Źródło pierwotne	Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).
Inne nazwy	Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model	TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder
Pokrewne	6	6
Podsumowanie	The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.	Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain data needed for high-quality generation or representation learning.