Comparer des méthodes

	Apprentissage par transfert avec autoencodeur variationnel	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014 (VAE); 2010 (transfer learning survey)	2014
Auteur d'origine	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang	Goodfellow, I. et al.
Type	Generative model with transferred encoder/decoder	Generative deep learning (adversarial two-network game)
Source fondatrice	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	6	4
Résumé	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.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.