השוואת שיטות
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| רשת עצבית גרפית× | סיווג תמונות באמצעות רשתות קונבולוציה (CNN)× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2017 | 2016 |
| הוגה השיטה≠ | Kipf, T.N. & Welling, M. | He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet) |
| סוג≠ | Deep learning on graph-structured data | Deep convolutional neural network (supervised) |
| מקור מכונן≠ | 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 ↗ |
| כינויים | 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 |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. |
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