Compară metode

	Învățare prin transfer	Autoencoder Variațional
Domeniu	Învățare automată	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2010 (formalized); 1990s (early roots)	2014
Autorul original	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Kingma, D. P. & Welling, M.
Tip	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Sursa seminală	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).
Denumiri alternative	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Înrudite	3	5
Rezumat	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.