# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 4 Experiments
### Results  
#### 4.3.1 Evaluation on ImageNet  
The proposed explanation methods are evaluated using ResNet50 and VGG16, both pretrained on ImageNet, and tested their performance on unseen ImageNet images using the SHAP library. These images were not part of the training data. The visual comparisons in Figures 2 and 3 demonstrate that for both ResNet50 and VGG16, expected gradients tends to highlight important features distributed in the background rather than on the object itself. As shown in Figures 2b and 3b, the EG method exhibits high response in background regions, whereas our prior-guided methods (Figures 2c,d and 3c,d) concentrate attribution signals on object-specific areas. Model-learned features should ideally be object-centric rather than background-related. The proposed deep prior-based baselines--DeepBGMM-EG and DeepMGM-EG--better align with the definition of \"missing\" and mitigate the attribution of background features to varying extents. These methods concentrate important features more effectively on the object, aligning better with human cognition and providing more faithful explanations of the model's reasoning. Comparing DeepBGMM-EG and DeepMGM-EG, it is clear that in most cases, DeepBGMM-EG emphasizes object outlines and object-specific features more effectively. As shown in Figures 4 and 5, ResNet50's attributions proved to be better than those of VGG16 due to the correct prediction of agama, whereas VGG16 incorrectly predicted starfish, which negatively affected the attribution results. From the feature attribution maps generated for the American egret using ResNet50 and VGG16, it is evident that the explanatory results of ResNet50 and the predictive results of VGG16 are suboptimal. This can be largely attributed to the models' prediction outcomes, as both ResNet50 and VGG16 identified the image as a crane rather than an American egret. Consequently, inaccurate predictions significantly affect the performance of the explanation methods used. We selected 50 images from the ImageNet validation set for each of the 12 categories, resulting in a total of 600 images, to compare the performance of the four explanation methods across the metrics presented in the table. Figure 3: In the original images of _bulbul_, _studio couch_, _orange_, _American egret_, and _ox_ (**a**), the prediction results of VGG16 are explained using expected gradients (**b**), DeepBGMM-EG (**c**), and DeepMGM-EG (**d**) methods, presenting the corresponding feature attribution maps. Figure 2: In the original images of _bulbul_, _studio couch_, _orange_, _American egret_, and ox (**a**), the prediction results of ResNet50 are explained using expected gradients (**b**), DeepBGMM-EG (**c**) and DeepMGM-EG (**d**) methods, presenting the corresponding feature attribution maps. The performance of EG, DeepBGMM-EG, DeepMGM-EG, and LIFT is compared for ResNet50 in Table 1. DeepBGMM-EG and DeepMGM-EG showed advantages in KPM, RNM, and RAM, outperforming other methods in handling feature importance and feature recovery. For KAM, both methods performed similarly to EG, indicating stability in handling important features. In contrast, EG demonstrated stronger performance on KNM and RPM, suggesting greater efficiency in recovering negative feature importance. From the perspective of feature recovery (KPM, KNM, KAM), gradually unmasking features based on their attributed importance (high to low) and observing prediction accuracy can reveal feature importance: significant accuracy improvement indicates higher importance, demonstrating the DeepPrior-EG's fidelity to the model. Conversely, from the feature masking perspective (RPM, RNM, RAM), gradually masking features (high to low) and observing accuracy can similarly identify important features: significant accuracy decline confirms higher importance, further validating the DeepPrior-EG's alignment with the model's behavior. In general, DeepBGMM-EG and DeepMGM-EG outperformed EG in most metrics, particularly in comprehensive assessments of the importance of features.