قارن الطرق

	المشفر التلقائي التبايني شبه المُشرف	التعلم بالنقل باستخدام المرمز التلقائي المتغير
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	2014 (VAE); 2010 (transfer learning survey)
صاحب الطريقة	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang
النوع	Generative probabilistic model (semi-supervised)	Generative model with transferred encoder/decoder
المصدر التأسيسي	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).
الأسماء البديلة	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
ذات صلة	6	6
الملخص	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.