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Benchmarks
Image Classification on CIFAR-10 AdvL
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98.67
Accuracy
defensive patch generation framework
60.9076
70.7113
80.515
90.3187
Apr 13, 2022
Accuracy
Updated 3mo ago
Evaluation Results
Method
Method
Links
Accuracy
defensive patch generation framework
Model=MNet, Setting=Fo...
2022.04
98.67
defensive patch generation framework
Model=VGG, Setting=Fou...
2022.04
97.27
defensive patch generation framework
Model=SNet, Setting=Fo...
2022.04
96.07
defensive patch generation framework
Model=RNet, Setting=Fo...
2022.04
96.04
UnAdv
Model=RNet, Setting=Fo...
2022.04
88.87
Vanilla
Model=RNet, Setting=Fo...
2022.04
85.65
Vanilla
Model=MNet, Setting=Fo...
2022.04
84.13
UnAdv
Model=VGG, Setting=Fou...
2022.04
83
Trans
Model=RNet, Setting=Fo...
2022.04
81.61
Vanilla
Model=VGG, Setting=Fou...
2022.04
80.4
Vanilla
Model=SNet, Setting=Fo...
2022.04
78.9
Trans
Model=MNet, Setting=Fo...
2022.04
78.65
Trans
Model=SNet, Setting=Fo...
2022.04
73.53
Trans
Model=VGG, Setting=Fou...
2022.04
72.71
UnAdv
Model=SNet, Setting=Fo...
2022.04
71.79
UnAdv
Model=MNet, Setting=Fo...
2022.04
62.36
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