NAP: Neural architecture search with pruning

There has been continuously increasing attention attracted by Neural Architecture Search (NAS). Due to its computational efficiency, gradient-based NAS methods like DARTS have become the most popular framework for NAS tasks. Nevertheless, as the search iterates, the derived model in previous NAS frame-works becomes dominated by skip-connects, causing the performance downfall. In this work, we present a novel approach to alleviate this issue, named Neural Architecture search with Pruning (NAP). Unlike prior differentiable architecture search works, our approach draws the idea from network pruning. We first train an over-parameterized network, including all candidate operations. Then we propose a criterion to prune the network. Based on a newly designed relaxation of architecture representation, NAP can derive the most potent model by removing trivial and redundant edges from the whole network topol-ogy. Experiments show the effectiveness of our proposed approach. Specifically, the model searched by NAP achieves state-of-the-art performances (2.48% test error) on CIFAR-10. We transfer the model to ImageNet and obtains a 25.1% test error with only 5.0 M parameters, which is on par with modern NAS methods. (c) 2021 Published by Elsevier B.V.