From: Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
Method | MNIST ResNet-18 | FMNIST ResNet-18 | SVHN ResNet-18 | CIFAR-10 ResNet-18 | CIFAR-10 Shake-Shake-96 | CIFAR-100 Shake-Shake-112 |
---|---|---|---|---|---|---|
GCCS (regular training) | 3.79 | 4.98 | 1.85 | 2.53 | 0.46 | 0.0034 |
GCCS (adversarial training) | 3.93 | 4.91 | 3.05 | 2.42 | 0.94 | 0.0051 |
No Defense (cross-entropy loss) | 1.83 | 0.55 | 1.73 | 1.43 | 0.27 | 0.0011 |
No Defense (adversarial training) | 1.10 | 0.45 | 0.52 | 0.48 | 0.06 | 0.0013 |
JR (regular training) [53] | 0.55 | 0.31 | 1.73 | 0.32 | - | - |
JR (adversarial training) [53] | 1.60 | 0.39 | 0.53 | 0.17 | - | - |
IGR (regular training) [44] | 0.48 | 0.37 | 1.54 | 0.83 | 0.05 | 0.0013 |
IGR (adversarial training) [44] | 1.79 | 0.46 | 0.58 | 0.46 | 0.06 | 0.0017 |
CLR (regular training) [52] | 0.31 | 0.29 | 0.33 | 0.26 | - | - |
CLR (adversarial training) [52] | 0.85 | 0.39 | 0.36 | 0.26 | - | - |