Graph Models
Diffusion-Convolutional Neural Networks
Introduced by Atwood et al. in Diffusion-Convolutional Neural NetworksDiffusion-convolutional neural networks (DCNN) is a model for graph-structured data. Through the introduction of a diffusion-convolution operation, diffusion-based representations can be learned from graph structured data and used as an effective basis for node classification.
Description and image from: Diffusion-Convolutional Neural Networks
Source: Diffusion-Convolutional Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| General Classification | 42 | 9.19% |
| Semantic Segmentation | 35 | 7.66% |
| Image Classification | 30 | 6.56% |
| Classification | 27 | 5.91% |
| Object Recognition | 18 | 3.94% |
| Image Segmentation | 15 | 3.28% |
| Object Detection | 12 | 2.63% |
| Face Recognition | 7 | 1.53% |
| Decoder | 5 | 1.09% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
