Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Neuraale Radiantie Velden (NeRF)× | Transformator voor Visuele Waarneming× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2020 | 2021 |
| Grondlegger≠ | Ben Mildenhall | Dosovitskiy, A. et al. |
| Type≠ | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Oorspronkelijke bron≠ | Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Aliassen≠ | NeRF, Neural radiance field | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality. | 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). |
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