## 2 Related Works
### Class Activation Map  
The class activation map (CAM) is a technique used to visualize the image classification process of a convolutional neural network (CNN) model, which is an interpretable artificial intelligence method. CAM can be used to explain to people what regions of an image the \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Related Works** & **Deficiencies** \\ \hline NASNet & Requires expensive hardware facilities and time, is prone to over-fitting, is usually optimized for specific tasks and datasets, finds optimal architectures that may not be applicable to other types of tasks, and needs to be researched or fine-tuned. \\ \hline ENAS & Limited to specific tasks and requires multiple reinforcement learning; the search process requires time and a large amount of computational resources. \\ \hline DARTS & Searches only one layer of the network structure, cannot search deeper network structures, easy to overfit. \\ \hline TE-NAS & Uses a genetic algorithm for search; has poor performance in high-dimensional search spaces and low search efficiency. \\ \hline Zen-NAS & Can be affected by the limitations of the pre-trained model; the pre-trained architecture may not generalize well to the target task, thus affecting performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deficiencies of existing NAS approaches. CNN model focuses on, and it can help people understand to what extent the CNN model focuses on different regions of the image when classifying an image. The class activation map technique was first proposed by [PERSON] et al. [16]. In a network structure with global average pooling (GAP), CAM can extract the key regions of the network through the GAP layer. When CNN is used for image classification, the model performs several convolution and pooling operations on the image to extract the features of the image, and adds a global average pooling layer after the last fully connected layer of the model. The global average pooling layer averages the feature maps of each channel to obtain a channel weight value. The final CAM is then obtained by multiplying and summing these channel weights with the corresponding feature maps, upsampling the CAM to the size of the original image, and overlaying it with the original image to create the visualization. Darker regions indicate that the model pays more attention to the region and, thus, has a greater impact on the classification results.The disadvantage of CAM is that it requires modification to the network structure and retraining of the network. Grad-CAM [17] proposed by [PERSON] et al. uses a gradient-based global average to compute the weights, thus overcoming the drawback of CAM, as it requires changes in the network structure and allows the extraction of features from any layer of the network. Grad-CAM++ [18], proposed by [PERSON] et al., improves Grad-CAM by improving the computation of weights, allowing for better interpretation of the model's predictions and the ability to localize multiple targets in a single image. [PERSON] et al. proposed Score-CAM [19], which computes the weights by forward-passing the scores of the target categories of the activation graph. The final result of Score-CAM is obtained by a linear combination of weights and the activation graphs obtained from a linear combination of the weights and the activation graphs without relying on the gradient for computation. [PERSON] et al. proposed SS-CAM (softmax-weighted class activation mapping) [20], which combines class activation mapping and softmax weighting to generate more accurate and interpretable category activation maps. The softmax values of each class are used as weights, and the feature maps of all classes are weighted and averaged, which takes into account the feature maps at different levels in the network, thus providing more comprehensive information to explain the model's classification decisions. [PERSON] et al.