So sánh phương pháp

	Học chuyển giao với Bộ Tự mã hóa Biến phân	Mô hình Autoencoder biến phân bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014 (VAE); 2010 (transfer learning survey)	2014
Người khởi xướng	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.
Loại	Generative model with transferred encoder/decoder	Generative probabilistic model (semi-supervised)
Công trình gốc	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).	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.
Tên gọi khác	TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder	Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model
Liên quan	6	6
Tóm tắt	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.	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.