Adversarial Robustness On Cifar 10
Métriques
Accuracy
Attack: AutoAttack
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy | Attack: AutoAttack |
|---|---|---|
| global-local-regularization-via | 84.13 | 49.94 |
| improving-the-accuracy-robustness-trade-off | 95.23 | 68.06 |
| stochastic-local-winner-takes-all-networks | 92.26 | 82.6 |
| robust-representation-learning-via-asymmetric | 81.70 | 59.70 |
| stochastic-local-winner-takes-all-networks | 91.88 | 81.22 |