Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Transformator voor Visuele Waarneming× | Generatief Adversarieel Netwerk× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2021 | 2014 |
| Grondlegger≠ | Dosovitskiy, A. et al. | Goodfellow, I. et al. |
| Type≠ | Transformer architecture for images (self-attention over patches) | Generative deep learning (adversarial two-network game) |
| Oorspronkelijke bron≠ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Aliassen | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | 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). | 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. |
| ScholarGateGegevensset ↗ |
|
|