## 4 Experiment  
### Model Selection Process  
This section compares different NAS models that do not need to be trained on CIFAR-10 and CIFAR-100 using the same search space, search strategy, and training settings. The experiments use the same search space as Zen-NAS. In the search space, the maximum depth of the neural network structure is limited to 6 layers; choices include convolutional kernels of sizes 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 sizes, with channel options of 8, 32, 64, 128, 192, and 256 channels, and with the flexibility to choose whether or not to use jump joins, ReLU activation functions, the Adam optimizer, and batch normalization. Different permutations of the search space affect the diversity, performance, interpretability, and computational efficiency of the neural network structures found by the search algorithm. By designing the search space appropriately, the search algorithm can better explore the possibilities of network structures and find a neural network model with a superior performance suitable for a specific task. The initial structure is a small randomly selected network that is guaranteed to satisfy the inference budget. To evaluate the performance of the NAS model, we will measure its accuracy and efficiency. Accuracy will be calculated based on the model's classification performance on the CIFAR-10 and CIFAR-100 validation datasets. Meanwhile, efficiency will be measured in terms of computational cost, including inference time and the number of model parameters. This is crucial for practical applications seeking efficient architectures. Through a comparative analysis, we aim to understand the strengths and limitations of each NAS model under the same search spaces and settings. Table 2 shows the different NAS proxies and their accuracies on the CIFAR-10 and CIFAR-100 datasets. Using different NAS agents and CAM-NAS involves finding high-performance neural network structures that are suitable for specific tasks through search algorithms. These proxies are the criteria used to evaluate the quality and performances of different network structures. For example, Zen-score denotes a quality evaluation metric used by Zen-NAS, FLOPs denotes the computation amount of the network structure, SynFlow refers to the mobility of the network structure, TE-score denotes the evaluation metric of TE-NAS, and random denotes the random search method. Table 2 lists these proxies, and their performances are evaluated using the same dataset and settings. The accuracy of CAM-NAS on the CIFAR-10 and CIFAR-100 datasets is acceptable and significantly better than a random search. This result shows that comparing the similarities of class activation maps generated by different models can be used as a new criterion to evaluate the quality of submodels. CAM-NAS has the highest accuracy on the CIFAR-10 and CIFAR-100 datasets compared to other zero-shot proxies. Figure 4 shows the time required by different agents to process five images under the same conditions. Among the four zero-shot NAS methods, TE-NAS is the slowest, while the proposed CAM-NAS is comparable to the Zen-score and NASWOT. CAM-NAS is many orders of magnitude faster than traditional NAS methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Proxy** & **CIFAR-10** & **CIFAR-100** \\ \hline Zen-Score & 96.2\% & 80.1\% \\ FLOPs & 93.1\% & 64.7\% \\ Grad & 92.8\% & 65.4\% \\ Synflow & 95.1\% & 75.9\% \\ TE-Score & 96.1\% & 77.2\% \\ NASWOT & 96.0\% & 77.5\% \\ Random & 93.5 \(\pm\) 0.7\% & 71.1 \(\pm\) 3.1\% \\ CAM-NAS & 95.68\% & 75.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different NAS proxy accuracies for the CIFAR-10 and CIFAR-100 datasets. The use of different NAS agents and CAM-NAS involves finding high-performance neural network structures that are suitable for specific tasks through search algorithms. These proxies are the criteria used to evaluate the quality and performances of different network structures.