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Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use learnable Gaussian mixture patch operators instead of spectral ChebNet filters | MoNet on MNIST superpixel graphs · MNIST 300 Superpixels | accuracy: 88.05% -> 97.30% | Helped |
| Use learnable Gaussian mixture patch operators instead of spectral ChebNet filters | MoNet on MNIST superpixel graphs · MNIST 150 Superpixels | accuracy: 80.94% -> 96.75% | Helped |
| Use learnable Gaussian mixture patch operators instead of spectral ChebNet filters | MoNet on MNIST superpixel graphs · MNIST 75 Superpixels | accuracy: 75.62% -> 91.11% | Helped |
| Use MoNet rather than LeNet5 | MNIST image classification on regular grids · MNIST *Full grid | accuracy: 99.33% -> 99.19% | No clear effect |
| Use MoNet rather than LeNet5 | MNIST image classification on regular grids · MNIST *1/4 grid | accuracy: 98.59% -> 98.16% | No clear effect |
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