## 3 Method
### CAM Techniques  
A series of experiments were conducted among known CAM techniques to determine which CAM techniques are suitable for integration with NAS algorithms. The experiments used the pre-trained ResNet family of models, including ResNet-18, ResNet-34, ResNet-101, and ResNet-152. For all ResNet models, the class activation mapping of the last layer was extracted using the CAM technique. ResNet-18 and ResNet-34 are shallower ResNet variants with slightly lower performances on some complex tasks. ResNet-50, ResNet-101, and ResNet-152 are deeper ResNet variants that are capable of extracting rich feature representations for more complex tasks. Because ResNet-152 is deeper and can capture richer features, it has been trained on large image datasets and has learned many useful  
Figure 1: Main process of CAM-NAS. Similarity scores are calculated by comparing the CAM maps of the teacher model and the child model. feature representations. Using it as a teacher model allows the student model to benefit from these rich features. And the teacher model should be a stable model with high accuracy to ensure that the knowledge transferred to the student model is reliable. ResNet-152 has been shown to be a stable and high-performing model in many tasks. Therefore, ResNet-152 was selected as the teacher model. Choosing the right CAM technique to combine with NAS algorithms is critical for an efficient submodel search and evaluation. Since the NAS algorithm requires a large number of model evaluations during the search process, it is important to extract the saliency map quickly and accurately. Based on the experimental results, we tend to choose the more computationally efficient and powerful CAM technique combined with the NAS algorithm to improve the search efficiency and performance. The class activation maps of other models were compared with ResNet-152 and the similarities between them were calculated. As shown in Figure 2, the longest time was spent when using SS-CAM and IS-CAM. This may be due to the fact that they consider more spatially sensitive features in the computation process, resulting in a corresponding increase in the computational load. In contrast, the other CAM methods use a more simplified computational strategy and, therefore, require less time to extract the saliency maps. In the study, methods such as smooth Grad-CAM++ and axiom-based Grad-CAM (XGradCAM) were used to extract the activation mapping of the images in the model, and the effectiveness of these methods was evaluated by comparing the similarity scores and accuracies. As shown in Figure 3, the results indicate that when using the smooth Grad-CAM++ and XGradCAM methods, there is no linear correlation between similarity scores and model accuracy on the ImageNet validation dataset, i.e., the changes in these two metrics are not identical. However, under the same experimental conditions, there was a linear correlation between the similarity score and model accuracy for the Grad-CAM, Grad-CAM++, and Score-CAM methods. This implies that there is a correlation between the similarity scores of activation mappings and model accuracy in these methods, i.e., activation mappings with high similarity scores may be associated with high model accuracy. Although the Score-CAM method has a linear correlation between similarity scores and model accuracy, it is slower to compute, especially when the network structure is deep. On the other hand, the similarity scores obtained with the Grad-CAM++ method are not very clear in distinguishing between different network structures. Therefore, the final choice is to use the Grad-CAM method in combination with NAS. This is due to the fact that the Grad-CAM method has a good linear correlation with model accuracy and high computational efficiency, which makes it the most suitable choice for activation mapping analysis during the neural network architecture search. By combining it  
Figure 2: Calculation of the time it takes to scan an image using different CAM methods.