So sánh phương pháp

	Mô hình Autoencoder biến phân bán giám sát	Học chuyển giao với Bộ Tự mã hóa Biến phân
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014	2014 (VAE); 2010 (transfer learning survey)
Người khởi xướng	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang
Loại	Generative probabilistic model (semi-supervised)	Generative model with transferred encoder/decoder
Công trình gốc	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).
Tên gọi khác	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
Liên quan	6	6
Tóm tắt	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.