Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Overføringslæring× | Vision Transformer× | |
|---|---|---|
| Fagfelt≠ | Maskinlæring | Dyp læring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2010 (formalized); 1990s (early roots) | 2021 |
| Opphavsperson≠ | 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) |
| Opprinnelig kilde≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Alias | TL, domain adaptation, fine-tuning, pre-trained model adaptation | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relaterte≠ | 3 | 5 |
| Sammendrag≠ | 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). |
| ScholarGateDatasett ↗ |
|
|