Semi-Supervised Classification with Graph Convolutional Networks @ICLR2017読み会
Dynamic filters in graph convolutional network
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Dynamic Filters in Graph Convolutional Networks, Verma,+, '17 2017年9月13日 @shima_x
Transcript of Dynamic filters in graph convolutional network
- 1. Dynamic Filters in Graph Convolutional Networks, Verma,+, '17 2017913 @shima_x
- 2. Agenda /
- 3. local filteringGCN 3D shape correspondanceSoTA
- 4. GC local graph convolution 3D shape correspondenceSoTA
- 5. GC 3
- 6. weight
- 7. Convolution E: out channel D: in channel F: filter w,h: width, height
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- 9. Convolution y = b + q (x ,x )W x q (x ,x ) exp(u x +v x + c ) q (x ,x ) = 1, q (x ,x ) = = 1 q (x ,x ): x ,x weight N : i+1i M: Weight matrixM i m=1 M N i 1 jNi m i j m j m i j m T i m T j m m=1 M m i j jNi N i 1 m=1 M m i j jNi N i 1 m i j i j i
- 10. Convolution q (x ,x ) = = 1 MLP q (x ,x ) exp(u x +v x + c ) N = M, q (x ,x ) {0, 1} jNi N i 1 m=1 M m i j jNi N i 1 m i j m T i m T j m i i m i j
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- 12. u = v q (x ,x ) exp(u (x x ) + c ) m m m ij i j m T j i m
- 13. MNIST Cora, PubMed FAUST
- 14. MNIST M=9 Pooling...
- 15. Cora and PubMed M=1 to 32 validationM=1
- 16. FAUST 10 shapes in 10 different poses each=100meshes 6,890 vertices each features: 3D XYG vertex or SHOTSHIFT M=9
- 17. FAUST
- 18. FAUSTclass