  As deep neural networks achieve unprecedented performance in various tasks,
neural architecture search (NAS), a research field for designing neural network
architectures with automated processes, is actively underway. More recently,
differentiable NAS has a great impact by reducing the search cost to the level
of training a single network. Besides, the search space that defines candidate
architectures to be searched directly affects the performance of the final
architecture. In this paper, we propose an adaptation scheme of the search
space by introducing a search scope. The effectiveness of proposed method is
demonstrated with ProxylessNAS for the image classification task. Furthermore,
we visualize the trajectory of architecture parameter updates and provide
insights to improve the architecture search.

Machine Learning