Mixture 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 CNNsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object | 3 | 5.56% |
Semantic Segmentation | 3 | 5.56% |
Optical Flow Estimation | 3 | 5.56% |
Recommendation Systems | 2 | 3.70% |
Object Detection | 2 | 3.70% |
Image Generation | 2 | 3.70% |
Object Discovery | 2 | 3.70% |
Unsupervised Object Segmentation | 2 | 3.70% |
Dictionary Learning | 1 | 1.85% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |