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Explainable AI Evaluation on Photobombing

66.01Area CoverageKernelShap
0.448417.469234.4951.5108Nov 13, 2024

Evaluation results

MethodLinks
KernelShapBackbone=ResNet502024.1166.0122.4625.2726.12—91.88
KernelShapBackbone=ResNet502024.1166.0122.4625.2726.12—91.88
Grad-CAM2024.1160.2519.0730.327.15—91.96
KernelShap2024.1151.6823.33736.65—91.86
ig2024.1145.9822.5148.0614.86—88.89
IGBackbone=ResNet502024.1141.8423.1151.7818.53—88.06
igBackbone=ResNet502024.1141.8423.1151.7818.53—88.06
GradientShap2024.1137.2624.6556.079.67—88.02
noisetunnel2024.1136.4724.4754.3910.12—88.35
GradientShapBackbone=ResNet502024.1129.623.7363.711.4—85.33
GradientShapBackbone=ResNet502024.1129.623.7363.711.4—85.33
rise2024.1128.0320.5661.1917.41—89.15
LRPBackbone=ResNet502024.1124.2920.560.4936.87—90.58
lrpBackbone=ResNet502024.1124.2920.560.4936.87—90.58
lime2024.1117.4814.5873.6517.78—88.91
NoiseTunnelBackbone=ResNet502024.1115.6518.3276.0413.48—87.32
noisetunnelBackbone=ResNet502024.1115.6518.3276.0413.48—87.32
rex2024.1114.0210.4668.8320.73—95.51
RISEBackbone=ResNet502024.117.879.4289.0629.77—87.98
riseBackbone=ResNet502024.117.879.4289.0629.77—87.98
LIMEBackbone=ResNet502024.115.075.2399.3730.46—89.21
limeBackbone=ResNet502024.115.075.2399.3730.46—89.21
Grad-CAMBackbone=ResNet502024.114.556.4298.4336.25—85.05
Grad-CAMBackbone=ResNet502024.114.556.4298.4336.25—85.05
REXBackbone=ResNet502024.112.972.6104.8129.47—97.39
rexBackbone=ResNet502024.112.972.6104.8129.47—97.39