MC-NAS:Visual Module Contribution Neural Architecture Search Method.

The existing NAS methods can not directly show the relationship between network models and candidate modules and the accuracy of model recognition. At the same time, many NAS methods have poor scalability and cannot extend their search strategies to arbitrary search space. In response to the above challenges, this paper proposes a visual module contribution neural architecture search method. In this paper, the concept of module contribution is first proposed, and the unified sampling principle in arbitrary search space is given by analyzing the dilemma of the contribution calculation process. Finally, the neural network architecture is generated through a dynamic network programming algorithm for specific constraints. Extensive experimental results demonstrate the effectiveness of the proposed algorithm. Using the CIFAR-10, CIFAR-100, and ImageNet16-120 datasets, the average accuracy on the NAS- Bench- 201 benchmark is 93.33%, 71.07%, and 42.69%, respectively. [ABSTRACT FROM AUTHOR]