Adversarial Robustness On Cifar 10
评估指标
Accuracy
Attack: AutoAttack
评测结果
各个模型在此基准测试上的表现结果
| Paper Title | Repository | |||
|---|---|---|---|---|
| Mixed classifier | 95.23 | 68.06 | Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing | |
| Stochastic-LWTA/PGD/WideResNet-34-10 | 92.26 | 82.6 | Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness | |
| Stochastic-LWTA/PGD/WideResNet-34-5 | 91.88 | 81.22 | Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness | |
| GLOT-DR | 84.13 | 49.94 | Global-Local Regularization Via Distributional Robustness | |
| TRADES-ANCRA/ResNet18 | 81.70 | 59.70 | Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria |
0 of 5 row(s) selected.