Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mtandao wa Neural wa Grafu× | Uainishaji wa Picha kwa CNN× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2017 | 2016 |
| Mwanzilishi≠ | Kipf, T.N. & Welling, M. | He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet) |
| Aina≠ | Deep learning on graph-structured data | Deep convolutional neural network (supervised) |
| Chanzo asilia≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ | He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR. DOI ↗ |
| Majina mbadala | Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network | CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet), convolutional neural network image classifier, deep image classification, ResNet / VGG / EfficientNet |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems. | CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks. |
| ScholarGateSeti ya data ↗ |
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