Σύγκριση μεθόδων

	Εκμάθηση μεταφοράς	Variational Autoencoder
Πεδίο	Μηχανική Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010 (formalized); 1990s (early roots)	2014
Δημιουργός	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Kingma, D. P. & Welling, M.
Τύπος	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Θεμελιώδης πηγή	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).
Εναλλακτικές ονομασίες	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Συναφείς	3	5
Σύνοψη	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.