## 5 Conclusions  
By combining neural network architecture search (NAS) and class activation mapping (CAM) techniques, we propose the CAM-NAS algorithm, which provides better interpretability and efficiency for the search process of neural network architectures. NAS is able to improve the model performance by automatically searching and optimizing the structure of neural networks, while the CAM technique helps us understand how different network architectures pay attention to different regions in the task Combining CAM techniques with NAS can provide a more in-depth explanation and guidance during the search process.  
Using CAM techniques, we can visualize regions of interest for different candidate network structures in an image classification task to better understand how NAS-searched network structures function in a given task. By observing the regions of interest displayed by CAM, researchers can adjust the design of the network structures to increase the focus on important regions and, thus, improve model performance. In addition, the CAM-NAS algorithm evaluates the quality of submodels by directly comparing the similarity between the teacher model and the last layer of the class activation graph of the submodels, avoiding the complex process of training submodels in the traditional NAS approach, thus greatly reducing the computational cost, and being able to complete the search and evaluation within 0.08 seconds.  
Experiments on the CIFAR-10 and CIFAR-100 datasets validate the effectiveness of CAM-NAS. Our results confirm that the accuracy of models retrieved via CAM-NAS outperforms the current state-of-the-art zero-shot NAS technology. This provides an empirical validation of our approach and highlights the potential of CAM-NAS to improve the neural network architecture search.  
In conclusion, the CAM-NAS algorithm is an important step toward a more efficient and interpretable neural network architecture search. By combining the focus of CAM techniques with the search process, we demonstrate the potential for building models with better performance and lower complexity. These results open up new directions for future research and encourage the exploration of more advanced architectures and optimization strategies based on CAM-NAS. In future work, we hope to explore the structure of the submodules themselves and discover the relationship between the model structure itself and the performance of the models on the dataset.  
Conceptualization, Z.Z.; methodology, Z.Z. and Z.W.; software, Z.Z.; validation, Z.Z., Z.W. and I.J.; writing--original draft preparation, Z.Z. and Z.W.; writing--review and editing, Z.Z., Z.W. and I.J.; visualization, Z.Z. and Z.W.; supervision, I.J.; project administration, Z.Z., Z.W. and I.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (no. 2020-0-00107, development of technology to automate the recommendations for big data analytic models that define data characteristics and problems).  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare that they have no conflict of interest.