## 5 Experiments
### Computational Cost of TFT  
In Algorithm 1, the bottleneck is computing the RPI scores for masked images at different threshold values, where each of them requires a forward pass of the target CNN to produce prediction probability for the class \(c\) for each masked image. We have tried \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Attribution** & \multicolumn{2}{c}{**ResNet-50**} & \multicolumn{2}{c}{**VGG-16**} \\ \cline{3-5}  & **Method** & **Original** & **AFT-Tuned** & **Original** & **AFT-Tuned** \\ \hline \multirow{4}{*}{ImageNet} & Grad-CAM & & 0.736 & & 0.720 \\  & Grad-CAM++ & & 0.737 & & 0.718 \\  & Ablation-CAM & & 0.737 & & 0.720 \\  & Layer-CAM & & 0.736 & & 0.719 \\ \hline \multirow{4}{*}{Pascal VOC} & Grad-CAM & & 0.922 & & 0.904 \\  & Grad-CAM++ & & 0.922 & & 0.905 \\ \cline{1-1}  & Ablation-CAM & & 0.922 & & 0.903 \\ \cline{1-1}  & Layer-CAM & & 0.922 & & 0.904 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of test set prediction accuracy rates of the original and the AFT-tuned CNNs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Attribution** & \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**ResNet-50**} & \multicolumn{2}{c}{**VGG-16**} \\ \cline{3-6}  & & & **Original** & **TFT** & **AFT** & **Original** & **TFT** & **AFT** \\ \hline \multirow{4}{*}{ImageNet} & Grad-CAM & Avg Increase & 0.334 & 0.439 (\(\times\)**1.32**) & 0.449 (\(\times\)**1.35**) & 0.294 & 0.385 (\(\times\)**1.31**) & 0.561 (\(\times\)**1.91**) \\  & Avg Drop & 0.130 & 0.117 (\(\times\)**1.11**) & 0.116 (\(\times\)**1.11**) & 0.159 & 0.144 (\(\times\)**1.10**) & 0.124 (\(\times\)**1.28**) \\ \cline{2-6}  & Grad-CAM++ & Avg Increase & 0.342 & 0.454 (\(\times\)**1.33**) & 0.507 (\(\times\)**1.48**) & 0.228 & 0.322 (\(\times\)**1.42**) & 0.505 (\(\times\)**2.22**) \\  & Avg Drop & 0.136 & 0.123 (\(\times\)**1.11**) & 0.105 (\(\times\)**1.29**) & 0.188 & 0.174 (\(\times\)**1.08**) & 0.153 (\(\times\)**1.23**) \\ \cline{2-6}  & Ablation-CAM & Avg Increase & 0.342 & 0.456 (\(\times\)**1.33**) & 0.514 (\(\times\)**1.50**) & 0.261 & 0.352 (\(\times\)**1.35**) & 0.496 (\(\times\)**1.90**) \\  & Avg Drop & 0.137 & 0.123 (\(\times\)**1.12**) & 0.109 (\(\times\)**1.25**) & 0.173 & 0.159 (\(\times\)**1.09**) & 0.136 (\(\times\)**1.27**) \\ \cline{2-6}  & Layer-CAM & Avg Increase & 0.340 & 0.452 (\(\times\)**1.33**) & 0.512 (\(\times\)**1.50**) & 0.230 & 0.325 (\(\times\)**1.42**) & 0.492 (\(\times\)**2.14**) \\  & Avg Drop & 0.128 & 0.124 (\(\times\)**1.11**) & 0.104 (\(\times\)**1.33**) & 0.187 & 0.172 (\(\times\)**1.09**) & 0.150 (\(\times\)**1.25**) \\ \hline \multirow{4}{*}{Pascal VOC} & Grad-CAM & Avg Increase & 0.546 & 0.679 (\(\times\)**1.24**) & 0.659 (\(\times\)**1.21**) & 0.569 & 0.670 (\(\times\)**1.17**) & 0.698 (\(\times\)**1.23**) \\  & Avg Drop & 0.055 & 0.045 (\(\times\)**1.20**) & 0.037 (\(\times\)**1.46**) & 0.054 & 0.045 (\(\times\)**1.20**) & 0.053 (\(\times\)**1.02**) \\ \cline{2-6}  & Grad-CAM++ & Avg Increase & 0.522 & 0.674 (\(\times\)**1.29**) & 0.618 (\(\times\)**1.18**) & 0.445 & 0.583 (\(\times\)**1.31**) & 0.618 (\(\times\)**1.39**) \\ \cline{2-6}  & Avg Drop & 0.059 & 0.046 (\(\times\)**1.28**) & 0.041 (\(\times\)**1.43**) & 0.067 & 0.056 (\(\times\)**1.20**) & 0.062 (\(\times\)**1.08**) \\ \cline{2-6}  & Ablation-CAM & Avg Increase & 0.523 & 0.682 (\(\times\)**1.31**) & 0.666 (\(\times\)**1.27**) & 0.519 & 0.638 (\(\times\)**1.23**) & 0.674 (\(\times\)**1.30**) \\  & Avg Drop & 0.049 & 0.036 (\(\times\)**1.34**) & 0.033 (\(\times\)**1.48**) & 0.052 & 0.044 (\(\times\)**1.18**) & 0.050 (\(\times\)**1.03**) \\ \cline{2-6}  & Layer-CAM & Avg Increase & 0.486 & 0.645 (\(\times\)**1.33**) & 0.620 (\(\times\)**1.28**) & 0.420 & 0.538 (\(\times\)**1.28**) & 0.562 (\(\times\)**1.34**) \\ \cline{2-6}  & Avg Drop & 0.063 & 0.051 (\(\times\)**1.23**) & 0.042 (\(\times\)**1.52**) & 0.069 & 0.058 (\(\times\)**1.19**) & 0.059 (\(\times\)**1.16**) \\ \hline \hline \end{tabular}
\end{table}