Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Трансформер для комп'ютерного зору× | Дифузійна модель× | |
|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2021 | 2020 |
| Автор методу≠ | Dosovitskiy, A. et al. | Ho, J., Jain, A. & Abbeel, P. |
| Тип≠ | Transformer architecture for images (self-attention over patches) | Generative deep learning (denoising diffusion) |
| Основоположне джерело≠ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Інші назви≠ | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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 diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling. |
| ScholarGateНабір даних ↗ |
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