# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 5 Discussion
### Evaluation of Class Activation Mapping Methods  
To further evaluate the quality of the explanations, we conducted a comparison using class activation mapping (CAM) and calculated insertion and deletion scores. These scores are analogous to the average drop (AD) and increase in confidence (IC) metrics, aiming to assess whether the importance of pixels in CAM maps aligns with the actual relevance of the image content [39]. Specifically, the deletion score measures how much the classification probability drops when important pixels are removed, while the insertion score tracks how much the probability increases when key pixels are added to a blank image. A lower deletion score and a higher insertion score indicate more interpretable and reasonable CAM maps.  
The calculation of the deletion (Del) score follows a specific procedure: pixels corresponding to an image are removed in descending order based on their weights in the class activation map (CAM). The original model is then utilized to compute the classification probabilities for the images after pixel removal. A curve is generated to illustrate the relationship between the classification probabilities and the proportion of removed pixels, and the area under this curve is calculated as the Del value.  
Similarly, the process for calculating the insertion (Ins) score is analogous to that of Del. Pixels are introduced in descending order of their weights, and the original model computes the classification probabilities for the images after pixel addition. A curve is created to represent the relationship between the classification probabilities and the proportion of introduced pixels, with the area under this curve being calculated as the Ins value.  
As illustrated in Table 4, the EG-based methods produced the most interpretable and coherent CAM maps, with DeepBGMM-EG achieving particularly strong results. DeepBGMM-EG had the highest insertion score and one of the lowest deletion scores,  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **KPM** & **KNM** & **KAM** & **RPM** & **RNM** & **RAM** \\ \hline EG & 1.0952 & \(\mathbf{-1.1014}\) & **0.9653** & **1.2502** & \(\mathbf{-1.2558}\) & 1.4305 \\ DeepBGMM-EG & **1.1193** & \(\mathbf{-1.1279}\) & 0.9580 & 1.2224 & \(\mathbf{-1.2340}\) & **1.4361** \\ Shape-EG & 1.1018 & \(\mathbf{-1.1044}\) & 0.9649 & 1.2485 & \(\mathbf{-1.2504}\) & 1.4317 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different explanation methods (EG, DeepBGMM-EG,Shape-EG) based on various metrics (KPM, KNM, KAM, RPM, RNM, RAM) for ResNet50 predictions. The dataset consists of 600 images selected from the ImageNet validation set, with 50 images per category from 12 categories.  
Figure 7: (**a**) Original Image (**b**) Annotated Contour Image.  
demonstrating its ability to effectively explain the decision-making process of the model. DeepMGM-EG and Shape-EG also performed well, consistently generating reasonable CAM visualizations. However, methods such as Lime and Grad-CAM, while offering some level of interpretability, were outperformed by the EG series in both metrics, highlighting the superiority of the proposed approach.  
In conclusion, the findings from the noise robustness and CAM comparison experiments further confirm the efficacy of the proposed methods. By integrating attribution priors, particularly through DeepBGMM-EG and DeepMGM-EG, the models demonstrate enhanced robustness to noise while producing more interpretable and meaningful feature importance maps. These results underscore the versatility and improved interpretability of these approaches across diverse scenarios, highlighting their potential to advance the robustness, transparency, and reliability of deep learning models in practical applications.