Image Classification Robustness on CIFAR-100 (test) (Clean and AutoAttack)
65Clean AccuracyAT(TrH)
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| AT(TrH)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 65 | 33 | |
| AT(SWA)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 64.1 | 33.7 | |
| TRADES(TrH)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 63.8 | 33 | |
| TRADES(S2O)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 63.5 | 31.5 | |
| AT(S2O)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 63.2 | 31.5 | |
| TRADES(SWA)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 62.7 | 33 | |
| AT(AWP)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 61.5 | 32.2 | |
| TRADES(AWP)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 60.8 | 32.2 | |
| AT(base)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 60 | 30.4 | |
| TRADES(base)Backbone=ViT-B16, Perturbation budget (epsilon)=8/255, Adversarial threat model=l_inf2022.11 | 58.6 | 30.5 |