# Towards a Reliable Evaluation of Local Interpretation Methods
## 3 Human-Friendly Evaluation
### Overview of Class-Discriminativeness Evaluation  
The class-discriminativeness refers to the ability of visual interpretation algorithms to help distinguish between classes. We assume that a good visual interpretation algorithm  
Figure 8: Flow chart of user persuasion method with three rounds (round1, round2 and round3), different colors indicate different round processes. Figure 7: Three visual interpretation algorithms retain 3 % and 8% feature occlusion maps. will only highlight features related to the predicted class, but will not highlight image areas of other class. Specifically, when an image contains two kinds of objects, the visual interpretation method should accurately highlight the category features related to the prediction result. For example, the model predicts a test case that contains both bananas and pineapples, and the final judgement result is bananas. The saliency map generated by the visual interpretation algorithm should only highlight features related to bananas, not pineapples. Pointing Game experiment is first introduced in [49] to evaluate the discriminativeness of different visualization methods for localizing target objects in scenes. Similar to [18], we also use Pointing Game to evaluate the discriminativeness of various visual explanation methods for localizing target objects in the image, which identify whether the prediction is related to the object in the image. we extract the maximum point on the different attention map and we call this the pointing game. Pointing Game asks the CNN model to point at an object of designated category in the image. Meanwhile, the pointing game does not require highlighting the full extent of an object. If the maximum point lies on one the instances of the object category, it is considered as hitting the object, otherwise a miss is counted. The overall performance of different interpretation method is measured by the average number  
Figure 10 shows the similarity result of 30 different samples with three different interpretation methods relative to the human attention (ground truth) mask. The similarity measurement result indicated the similarity between the FSM obtained by the interpretation method and the human-attention mask. The smaller the value, the more similar. At the same time, we calculated the average value of each interpretation algorithm relative to ground-truth. As shown in the Table 1, Grad-cam interpretation algorithm had the lowest MAE value, which is the best correctness. The correctness of rise interpretation method was ranked second. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interpretation Methods** & **Grad-Cam** & **Mask** & **Rise** \\ \hline MAE & 0.23 & 0.43 & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy results of different interpretation algorithms (the smallest value is the best). Figure 10: The similarity measure result of the feature importance of different interpretation methods relative to the ground truth value on the DataS data (Police car, Ambulance, School Bus, Tank, Cat, Goat, Panda, Polar Bear, Elephant and Uniform). Figure 9: Examples of ground truth result of the relevant feature block marked by the user of each category. (**TOP**) The 10 input image samples in 10 categories from the evaluation data set DataS. (**Middle**) Segmented image with 100 feature blocks obtained according to the SLIC segmentation algorithm. (**Bottom**) Resulting multi-participants human attention mask. Each image is annotated by 40 unique participants. We statistically calculated the confusion matrix (Table 2) of different visual interpretation algorithms. In order to quantitatively evaluate different visual interpretation algorithms, We have calculated the F1-score (Table 3) of each different interpretation method. We found that when a different evaluation metric was used to measure the correctness of the interpretation method, the results were completely consistent.