Comparer des méthodes

	Apprentissage par transfert avec autoencodeur variationnel	Apprentissage par transfert avec réseau neuronal convolutif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014 (VAE); 2010 (transfer learning survey)	2010–2014
Auteur d'origine	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Type	Generative model with transferred encoder/decoder	Transfer learning applied to convolutional neural networks
Source fondatrice	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
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.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.