Comparer des méthodes

	Apprentissage par transfert	Vision Transformer
Domaine	Apprentissage automatique	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010 (formalized); 1990s (early roots)	2021
Auteur d'origine	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Dosovitskiy, A. et al.
Type	Learning paradigm	Transformer architecture for images (self-attention over patches)
Source fondatrice	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Alias	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Apparentées	3	5
Résumé	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).