add new models and evaluations

model_info/cifar10/L2/Amini2024MeanSparse_S-WRN-70-16.json

@@ -0,0 +1,17 @@
+{
+  "link": "https://arxiv.org/abs/2406.05927",
+  "name": "MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification",
+  "authors": "Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr",
+  "additional_data": true,
+  "number_forward_passes": 1,
+  "dataset": "cifar10",
+  "venue": "arXiv, Jun 2024",
+  "architecture": "MeanSparse WideResNet-70-16",
+  "eps": "0.5",
+  "clean_acc": "95.51",
+  "reported": "87.28",
+  "autoattack_acc": "87.28",
+  "external": "84.33",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained model Wang2023Better_WRN-70-16. 84.33% robust accuracy is due to APGD (both versions) with BPDA.",
+  "unreliable": false
+}

model_info/cifar10/Linf/Amini2024MeanSparse.json → model_info/cifar10/Linf/Amini2024MeanSparse_Ra_WRN_70_16.json

@@ -11,6 +11,7 @@
   "clean_acc": "93.24",
   "reported": "72.08",
   "autoattack_acc": "72.08",
-  "footnote": "It adds the MeanSparse operator to the adversarially trained models.",
+  "external": "68.94",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained model Peng2023Robust. 68.94% robust accuracy is due to APGD (both versions) with BPDA.",
   "unreliable": false
 }

model_info/cifar10/Linf/Amini2024MeanSparse_S-WRN-94-16.json

@@ -0,0 +1,17 @@
+{
+  "link": "https://arxiv.org/abs/2406.05927",
+  "name": "MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification",
+  "authors": "Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr",
+  "additional_data": true,
+  "number_forward_passes": 1,
+  "dataset": "cifar10",
+  "venue": "arXiv, Jun 2024",
+  "architecture": "MeanSparse WideResNet-94-16",
+  "eps": "8/255",
+  "clean_acc": "93.60",
+  "reported": "75.28",
+  "autoattack_acc": "75.28",
+  "external": "73.10",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained model Bartoldson2024Adversarial_WRN-94-16. 73.10% robust accuracy is due to APGD (both versions) with BPDA.",
+  "unreliable": false
+}

model_info/cifar100/Linf/Amini2024MeanSparse_S-WRN-70-16.json

@@ -0,0 +1,17 @@
+{
+  "link": "https://arxiv.org/abs/2406.05927",
+  "name": "MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification",
+  "authors": "Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr",
+  "additional_data": true,
+  "number_forward_passes": 1,
+  "dataset": "cifar100",
+  "venue": "arXiv, Jun 2024",
+  "architecture": "MeanSparse WideResNet-70-16",
+  "eps": "8/255",
+  "clean_acc": "75.13",
+  "reported": "44.78",
+  "autoattack_acc": "44.78",
+  "external": "42.25",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained model Wang2023Better_WRN-70-16. 42.25% robust accuracy is due to APGD (both versions) with BPDA.",
+  "unreliable": false
+}

model_info/cifar100/Linf/Wang2023Better_WRN-28-10.json

@@ -11,6 +11,7 @@
   "clean_acc": "72.58",
   "reported": "38.83",
   "autoattack_acc": "38.83",
-  "footnote": "It uses additional 50M synthetic images in training.",
+  "external": "38.77",
+  "footnote": "It uses additional 50M synthetic images in training. 38.77% robust accuracy is given by MALT (Melamed et al., 2024).",
   "unreliable": false
 }

model_info/cifar100/Linf/Wang2023Better_WRN-70-16.json

@@ -11,6 +11,7 @@
   "clean_acc": "75.22",
   "reported": "42.67",
   "autoattack_acc": "42.67",
-  "footnote": "It uses additional 50M synthetic images in training.",
+  "external": "42.66",
+  "footnote": "It uses additional 50M synthetic images in training. 42.66% robust accuracy is given by MALT (Melamed et al., 2024).",
   "unreliable": false
 }

model_info/imagenet/Linf/Amini2024MeanSparse.json → model_info/imagenet/Linf/Amini2024MeanSparse_ConvNeXt-L.json

@@ -8,9 +8,10 @@
   "venue": "arXiv, Jun 2024",
   "architecture": "MeanSparse ConvNeXt-L",
   "eps": "4/255",
-  "clean_acc": "77.96",
+  "clean_acc": "77.92",
   "reported": "59.64",
   "autoattack_acc": "59.64",
-  "footnote": "It adds the MeanSparse operator to the adversarially trained models.",
+  "external": "58.22",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained models Liu2023Comprehensive_ConvNeXt-L. 58.22% robust accuracy is due to APGD (both versions) with BPDA.",
   "unreliable": false
 }

model_info/imagenet/Linf/Amini2024MeanSparse_Swin-L.json

@@ -0,0 +1,17 @@
+{
+  "link": "https://arxiv.org/abs/2406.05927",
+  "name": "MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification",
+  "authors": "Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr",
+  "additional_data": false,
+  "number_forward_passes": 1,
+  "dataset": "imagenet",
+  "venue": "arXiv, Jun 2024",
+  "architecture": "MeanSparse Swin-L",
+  "eps": "4/255",
+  "clean_acc": "78.80",
+  "reported": "62.12",
+  "autoattack_acc": "62.12",
+  "external": "58.92",
+  "footnote": "It adds the MeanSparse operator to the adversarially trained models Liu2023Comprehensive_Swin-L. 58.92% robust accuracy is due to APGD (both versions) with BPDA.",
+  "unreliable": false
+}

model_info/imagenet/Linf/RodriguezMunoz2024Characterizing_Swin-B.json

@@ -0,0 +1,15 @@
+{
+  "link": "https://arxiv.org/abs/2409.20139",
+  "name": "Characterizing Model Robustness via Natural Input Gradients",
+  "authors": "Adrián Rodríguez-Muñoz, Tongzhou Wang, Antonio Torralba",
+  "additional_data": false,
+  "number_forward_passes": 1,
+  "dataset": "imagenet",
+  "venue": "arXiv, Sep 2024",
+  "architecture": "Swin-B",
+  "eps": "4/255",
+  "clean_acc": "77.76",
+  "reported": "51.56",
+  "autoattack_acc": "51.56",
+  "unreliable": false
+}

model_info/imagenet/Linf/RodriguezMunoz2024Characterizing_Swin-L.json

@@ -0,0 +1,15 @@
+{
+  "link": "https://arxiv.org/abs/2409.20139",
+  "name": "Characterizing Model Robustness via Natural Input Gradients",
+  "authors": "Adrián Rodríguez-Muñoz, Tongzhou Wang, Antonio Torralba",
+  "additional_data": false,
+  "number_forward_passes": 1,
+  "dataset": "imagenet",
+  "venue": "arXiv, Sep 2024",
+  "architecture": "Swin-L",
+  "eps": "4/255",
+  "clean_acc": "79.36",
+  "reported": "53.82",
+  "autoattack_acc": "53.82",
+  "unreliable": false
+}