Comparer des méthodes

	Variational Autoencoder affiné	Réseau neuronal convolutif affiné
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014 (VAE); fine-tuning practice from 2015 onward	2012–2014
Auteur d'origine	Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
Type	Generative model with fine-tuning	Transfer learning technique (supervised fine-tuning)
Source fondatrice	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
Alias	fine-tuned VAE, domain-adapted VAE, transfer-learned VAE, adapted variational autoencoder	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
Apparentées	6	5
Résumé	A Fine-Tuned Variational Autoencoder begins with a VAE pre-trained on a large source dataset and then continues training on a smaller target-domain dataset. This approach adapts the learned latent representation and generative capacity to new data, preserving general structure while specializing to the target distribution — yielding better results than training from scratch when labeled or large target data is scarce.	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.