## 2 Related Works  
### Neural Architecture Search  
Many researchers have proposed different search strategies for finding neural architectures.  
[PERSON] et al. first proposed a reinforcement learning NAS method [9], which uses a recurrent neural network as a controller to generate a network and uses the accuracy of the searched network on the dataset as feedback, which is then passed to the controller through reinforcement learning to enable the controller to generate a new network. However, this NAS approach requires significant computational resources and long network search times. To speed up the search process, [PERSON] et al. proposed NASNet [10], which searches for units on small datasets and then stacks the searched small units to form a complete network for larger datasets.  
[PERSON] et al. proposed an efficient neural architecture search (ENAS) [11], which transforms the process of searching for neural network architectures into a trainable reinforcement learning problem, where each candidate architecture shares the same weights to improve search efficiency; it is able to search for high-performance network architectures using relatively few computational resources. This approach uses reinforcement learning to provide an efficient and cost-effective search for high-performance neural network architectures, reducing the search time to less than 16 hours via a single NVIDIA(r) GTX1080 Ti GPU. All of the above NAS methods treat the network structure in the search space as a discrete space.  
[PERSON] et al. proposed the Darts method [12], which transforms the network structure search into an optimization problem in a continuous space and uses gradient descent to find the optimal network structure. Some scholars proposed the concept of zero-shot NAS, which avoids the training process involving submodels, greatly speeding up the evaluation time of each submodel and improving the search efficiency. [PERSON] et al. proposed TENAS [13], which predicts the accuracy of submodels on a dataset by analyzing the number of neural tangent kernels and linear regions. [PERSON] et al. proposed the NASWOT (neural architecture search without training) [14], which calculates the number of activations of the ReLU units in the network and then measures the similarity of the inputs using the Hamming distance; the lower the similarity, the higher the score, which indicates the accuracy of the submodel. This replaces the traditional training process and reduces the search time to less than 1 s. [PERSON] et al. proposed another zero-shot NAS algorithm, ZenNAS [15], which searches the network in 0.1 seconds and focuses only on the last layer of information in the network, calculating the Gaussian complexity as a Zen-score, with higher scores representing a better network structure. However, none of these methods can explain why the structures found are very different from those designed by experts.  
ENAS is limited to specific tasks and uses reinforcement learning algorithms for specific tasks when searching for network structures, so the obtained structures may perform poorly on other tasks, requiring multiple reinforcement learning training, and the search process requires a large amount of computational resources and time. DARTS searches only one layer of the network structure in the search space and cannot search deeper complex network structures, which are prone to overfitting during the search process, resulting in poor performance of the searched structure on the validation set. TE-NAS uses genetic algorithms to perform the search; however, genetic algorithms can perform poorly in high-dimensional search spaces with low search efficiency, and there is a need to extract topologies from pre-trained models. The quality of these pre-trained models directly influences the search results. Zen-NAS uses the zero-shot NAS approach, which works better on certain tasks, but may not perform well on other tasks. The deficiencies of existing NAS methods are shown in Table 1. To solve the above problems, CAM-NAS is proposed.