方法证据记录
Vision Transformer
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).
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Vision Transformer (ViT)
分类方法记录 · ml-model / deep-learning
- Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. · URL
- Touvron, H. et al. (2021). Training Data-Efficient Image Transformers. ICML. · URL
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