# OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model
## 4 Experimental Results and Discussion  
### Experimental Results  
In this section, we conduct experiments to evaluate the effectiveness of the proposed interpretation methods. First, we qualitatively evaluated our method using ImageNet visualization to demonstrate the effectiveness of class conditional location of objects in a given image. In our experiment, we used the publicly available object classification dataset, i.e., ILSVRC2012 and PASCAL VOC 2007. We used H = W = 224 throughout. The ILSVRC2012_img_val dataset from ImageNet contains 50,000 images, 50 of each type. These categories correspond to the set of 1000 synonyms in WordNet. If an image contains x, it belongs to category x, where x is a synonym. The PASCAL VOC 2007 standard dataset is a benchmark for measuring the ability to classify images. The dataset contains 5011 images in the training set and 4952 images in the test set, for a total of 9963 images with 20 categories. The evaluation was performed for the top-1 and top-5 predicted categories, and 5000 images were selected from the dataset for evaluation. Given an image, we first obtained category predictions from our network and then generated OISE saliency maps for each predicted category. We used the pre-trained VGG-16 [27], GoogleNet [28], and Resnet50 [29] to evaluate OISE. After evaluating ILSVRC2012 and PASCAL VOC 2007, we report the values set in the localization error table for the top-1 and top-5 rankings in Table 1. In all three classical neural networks, OISE has a lower localization error than CAM and Grad-CAM. CAM and Grad-CAM require changes in the model structure and must be retrained, resulting in a worse classification error, while OISE improves in classification performance. The accuracy is shown in Table 2 for the ILSVRC2012 and PASCAL VOC07 datasets. OISE performs with consistently high accuracy. As shown in Table 3, OISE has an average decrease rate of 47.4% and an average increase rate of 19.7%, which is better than other CAM-based methods. The original input is masked by point-wise multiplication with the saliency maps to observe the score change on the target class. We follow the metrics used in [25] to measure the quality, the average drop is expressed as \(\sum_{i=1}^{N}\frac{max(0,Y_{i}^{c}-O_{i}^{c})}{Y_{i}^{c}}\times 100\) and the average increase is expressed as \(\sum_{i=1}^{N}\frac{Sigm(Y_{i}^{c}<O_{i}^{c})}{N}\), where \(Y_{i}^{c}\) is the predicted score for class c on image im and \(O_{i}^{c}\) is the predicted score for class c with the explanation map region as input. The experiment is performed on the ILSVRC2012 validation set; 2000 images are randomly selected. OISE performs well in the recognition task and can successfully detect distinguishable regions of the target objects. The results of the recognition task show that OISE better reflects the decision process of the original CNN model than previous methods. The statistical graph of the comparison results is shown in Figure 3.  
We qualitatively compared the saliency maps generated by the three methods (CAM, Grad-CAM, and OISE). Our method produces a more intuitively explainable saliency map with less random noise. The results are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Classification**} & \multicolumn{2}{c}{**Localization**} \\ \cline{3-6}  & & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline \multirow{3}{*}{VGG-16} & CAM & 33.40 & 12.20 & 57.20 & 45.14 \\ \cline{2-6}  & Grad-CAM & 30.38 & 10.89 & 56.51 & 46.41 \\ \cline{2-6}  & OISE & 30.31 & 10.23 & 56.82 & 46.38 \\ \hline \multirow{3}{*}{GoogleNet} & CAM & 31.9 & 11.3 & 60.09 & 49.34 \\ \cline{2-6}  & Grad-CAM & 31.9 & 11.3 & 60.09 & 49.34 \\ \cline{2-6}  & OISE & 30.8 & 10.6 & 60.89 & 49.12 \\ \hline \multirow{3}{*}{Resnet50} & CAM & 32.4 & 11.9 & 58.06 & 48.62 \\ \cline{2-6}  & Grad-CAM & 31.7 & 10.6 & 57.62 & 47.28 \\ \cline{2-6}  & OISE & 30.6 & 10.1 & 56.74 & 46.32 \\ \hline \hline \end{tabular}
\end{table}