Deep Learning Testing on CIFAR10
612Mean Failure CountSINVAD
Evaluation results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| SINVADModel=ResNet182026.06 | 612 | 41.8 | 39 | 2.9 | 13.1 | 1.1 | — | |
| SINVADModel=VGG162026.06 | 684 | 45.2 | 43 | 3.4 | 13.5 | 0.9 | — | |
| SUNTestModel=ResNet182026.06 | 1,981.5 | — | 59 | — | — | — | 0.86 | |
| SUNTestModel=VGG162026.06 | 2,092.2 | — | 65 | — | — | — | 0.66 | |
| MimicryModel=ResNet182026.06 | 4,062 | 118.5 | 98 | 0.7 | 39.2 | 1.5 | — | |
| NSGenModel=ResNet182026.06 | 4,088.2 | — | 100 | — | — | — | 1.09 | |
| NSGenModel=VGG162026.06 | 4,123.7 | — | 100 | — | — | — | 0.83 | |
| BAYESWARP-CModel=VGG162026.06 | 4,216.4 | — | 100 | — | — | — | 0.41 | |
| MimicryModel=VGG162026.06 | 4,287 | 124.3 | 98 | 0.9 | 41.8 | 1.6 | — | |
| BAYESWARP-IModel=VGG162026.06 | 4,389.2 | — | 100 | — | — | — | 0.35 | |
| BAYESWARP-CModel=ResNet182026.06 | 4,604.2 | — | 100 | — | — | — | 0.39 | |
| BAYESWARP-SModel=VGG162026.06 | 4,752 | — | 100 | — | — | — | 0.31 | |
| LATTEModel=ResNet182026.06 | 4,802 | 142.7 | 100 | 0 | 9.1 | 0.5 | — | |
| BAYESWARP-IModel=ResNet182026.06 | 4,845.6 | — | 100 | — | — | — | 0.34 | |
| BAYESWARP-SModel=ResNet182026.06 | 5,264.6 | — | 100 | — | — | — | 0.32 | |
| LATTEModel=VGG162026.06 | 5,513 | 168.4 | 100 | 0 | 9.6 | 0.5 | — |