Set Containment on ModelNet40 |S|=256
94AccuracyMASNET-ReLU
Evaluation results
| Method | Links | |
|---|---|---|
| MASNET-ReLU|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 94 | |
| MASNET-Hat|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 81 | |
| FlexSubNet|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 75 | |
| DeepSets|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 51 | |
| SetTransformer|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 51 | |
| Neural SFE|S|=256, Class ratio=1:1, Noise (std)=0.005, m (embedding dimension)=50, d (point dimension)=32025.10 | 51 |