Deep Learning Testing on ImageNet
100Seed Coverage MeanLATTE
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| LATTEModel=VGG192026.06 | 100 | 6,137 | 205.1 | 0 | 53.7 | 2.2 | — | — | |
| LATTEModel=ResNet502026.06 | 100 | 6,284 | 218.4 | 0 | 51.3 | 1.9 | — | — | |
| NSGenModel=VGG192026.06 | 100 | — | — | — | — | — | 4,633.4 | 2.16 | |
| BAYESWARP-CModel=VGG192026.06 | 100 | — | — | — | — | — | 5,206.5 | 1.78 | |
| BAYESWARP-IModel=VGG192026.06 | 100 | — | — | — | — | — | 6,149.7 | 1.51 | |
| BAYESWARP-SModel=VGG192026.06 | 100 | — | — | — | — | — | 5,754.1 | 1.77 | |
| NSGenModel=ResNet502026.06 | 100 | — | — | — | — | — | 4,392.6 | 1.87 | |
| BAYESWARP-CModel=ResNet502026.06 | 100 | — | — | — | — | — | 5,114.7 | 1.95 | |
| BAYESWARP-IModel=ResNet502026.06 | 100 | — | — | — | — | — | 6,235.7 | 1.77 | |
| BAYESWARP-SModel=ResNet502026.06 | 100 | — | — | — | — | — | 5,604.2 | 1.97 | |
| MimicryModel=VGG192026.06 | 98 | 5,614 | 164.2 | 1.3 | 214.3 | 9.4 | — | — | |
| MimicryModel=ResNet502026.06 | 98 | 5,429 | 152.6 | 1.1 | 198.4 | 8.2 | — | — | |
| ADAPTModel=ResNet502026.06 | 83 | — | — | — | — | — | 2,842.5 | 2.21 | |
| ADAPTModel=VGG192026.06 | 81 | — | — | — | — | — | 3,937.3 | 2.07 | |
| SINVADModel=VGG192026.06 | 44 | 942 | 62.4 | 4.1 | 68.4 | 4.5 | — | — | |
| SINVADModel=ResNet502026.06 | 42 | 815 | 58.7 | 3.8 | 72.3 | 5.1 | — | — |