## 1 Introduction  
In the early stages of deep learning development, the manual design of neural network structures was predominant. Nowadays, deep learning techniques have been applied to many research areas in computer vision and have achieved very good results in image classification [1, 2], biometrics [3], and medical diagnosis [4]. These successes are attributed to the efforts of experts who have designed various powerful network architectures, such as VGG [5], ResNet [6], and DenseNet [7]. However, as the complexity and size of deep learning models continue to increase, manually designing neural networks becomes more difficult and time-consuming. Designing a high-performance neural network structure requires a great deal of trial and error and experiences, often costing a great deal of time and computational resources, and it is difficult for experts to design a network that satisfies specific conditions. In addition, traditional neural network structures may be limited by human creativity and experience in discovering superior structures. To address the tediousness and limitations of manually designing neural network structures, the need for an automated neural network architecture search has emerged. Therefore, some researchers have proposed that algorithms can be designed to assist in the search for candidate architectures, rather than the entire network directly. A neural architecture search (NAS) is an algorithm designed to identify the most effective deep learning network by automatically searching and optimizing the network structure, reducing the burden of manual design efforts while searching for better network architectures to automatically find a better performing and efficient neural network structure for a given task and dataset for a specific problem. In early NAS methods, it was common to directly train the entire network using brute force methods to gauge its accuracy. These NAS methods require many computing resources, usually hundreds of GPUs, and many days to obtain the results. To save time and computational resources, newer methods have been proposed that do not rely on manual design efforts, but use reinforcement learning, genetic algorithms, or other optimization techniques to automatically find the best architecture from candidate neural network structures, such as chunking-based adaptive variational neural structure search algorithms [8]. However, existing NAS algorithms are still too slow in some cases due to internal training steps. The CAM (class activation mapping) family of algorithms is currently the most widely used method for interpretable artificial intelligence (XAI), and is capable of generating image-level heat maps that intuitively show the areas of the model's regions of interest, which helps to explain the underlying reasons for the model's predictions; the CAM family of algorithms is relatively simple and easy to implement, with a reasonably high degree of accuracy compared to other XAI methods. Because it relies heavily on the feature maps of convolutional layers, it is often more efficient than some methods that require repeated model runs or extensive sampling. Therefore, the CAM algorithm was chosen to be combined with NAS to provide greater opportunities for neural network interpretability and performance optimization. Compared with traditional NAS, which requires a training process, CAM-NAS can improve the search speed by several orders of magnitude, taking as little as 0.08 seconds to determine a structure. Numerous experiments have shown that CAM-NAS finds network structures with high accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets.