Adversarial Detection on PGD perturbations (val)
99.5AUROCMMD-CC + SimCLRv2
Evaluation Results
| Method | Links | |
|---|---|---|
| MMD-CC + SimCLRv2n_samples=20, Backbone=ResNet-502022.10 | 99.5 | |
| MMD + SimCLRv2n_samples=20, Backbone=ResNet-502022.10 | 98.8 | |
| MMD-CC + SimCLRv2n_samples=10, Backbone=ResNet-502022.10 | 96.6 | |
| MMD + SimCLRv2n_samples=10, Backbone=ResNet-502022.10 | 86.6 | |
| MMD-CC + Supervisedn_samples=20, Backbone=ResNet-502022.10 | 85.8 | |
| MMD-CC + SimCLRv2n_samples=5, Backbone=ResNet-502022.10 | 84 | |
| MMD-CC + SimCLRv2n_samples=3, Backbone=ResNet-502022.10 | 70.5 | |
| MMD-CC + Supervisedn_samples=10, Backbone=ResNet-502022.10 | 70.5 | |
| MMD-CC + Supervisedn_samples=5, Backbone=ResNet-502022.10 | 61.3 | |
| MMD + Supervisedn_samples=20, Backbone=ResNet-502022.10 | 57.5 | |
| MMD-CC + Supervisedn_samples=3, Backbone=ResNet-502022.10 | 57.4 | |
| MMD + SimCLRv2n_samples=5, Backbone=ResNet-502022.10 | 53.8 | |
| MMD + SimCLRv2n_samples=3, Backbone=ResNet-502022.10 | 35.2 | |
| MMD + Supervisedn_samples=10, Backbone=ResNet-502022.10 | 33 | |
| MMD + Supervisedn_samples=5, Backbone=ResNet-502022.10 | 22.5 | |
| MMD + Supervisedn_samples=3, Backbone=ResNet-502022.10 | 20 |