Designing of search space is a critical problem for neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms. This search space facilitates direct selection of channel numbers and kernel sizes in convolutions. In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training. The algorithm is further accelerated by a dynamic network shrinkage technique. Instead of a search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost. We open our entire codebase at: https://github.com/meijieru/AtomNAS

该文章提出了一种细粒度的搜索空间，有效地解决了神经体系结构搜索(NAS)中的搜索空间设计问题，其中包括资源感知的架构搜索框架和动态网络收缩技术，可同时进行搜索和训练，并在ImageNet上以小的搜索成本取得了最先进的性能。

AtomNAS：细粒度端到端神经架构搜索