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| Bildklassificering× | Vision Transformer× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2012 (deep CNN era); conceptual roots 1989 (LeCun) | 2021 |
| Upphovsperson≠ | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. | Dosovitskiy, A. et al. |
| Typ≠ | Supervised classification task | Transformer architecture for images (self-attention over patches) |
| Ursprungskälla≠ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Alias | visual classification, image recognition, CNN-based classification, visual categorization | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. | 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). |
| ScholarGateDatamängd ↗ |
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