Módszerek összehasonlítása

	Transzfer tanulás	Variációs Autoencoder
Tudományterület	Gépi tanulás	Mélytanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2010 (formalized); 1990s (early roots)	2014
Megalkotó	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Kingma, D. P. & Welling, M.
Típus	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Alapmű	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alternatív nevek	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Kapcsolódó	3	5
Összefoglaló	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.	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.