Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Generatiivne võistlev võrk× | Vision Transformer× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2014 | 2021 |
| Looja≠ | Goodfellow, I. et al. | Dosovitskiy, A. et al. |
| Tüüp≠ | Generative deep learning (adversarial two-network game) | Transformer architecture for images (self-attention over patches) |
| Algallikas≠ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Rööpnimetused | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | 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. | 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). |
| ScholarGateAndmestik ↗ |
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