Graph Models
Mixture model network
Introduced by Monti et al. in Geometric deep learning on graphs and manifolds using mixture model CNNsMixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds.
Image and description from: Geometric deep learning on graphs and manifolds using mixture model CNNs
Source: Geometric deep learning on graphs and manifolds using mixture model CNNs
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Optical Flow Estimation | 3 | 7.89% |
| Object Detection | 2 | 5.26% |
| Image Generation | 2 | 5.26% |
| Object Discovery | 2 | 5.26% |
| Semantic Segmentation | 2 | 5.26% |
| Unsupervised Object Segmentation | 2 | 5.26% |
| Dialogue State Tracking | 1 | 2.63% |
| Action Unit Detection | 1 | 2.63% |
| Facial Action Unit Detection | 1 | 2.63% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
