  Neural Architecture Search (NAS) has demonstrated state-of-the-art
performance on various computer vision tasks. Despite the superior performance
achieved, the efficiency and generality of existing methods are highly valued
due to their high computational complexity and low generality. In this paper,
we propose an efficient and unified NAS framework termed DDPNAS via dynamic
distribution pruning, facilitating a theoretical bound on accuracy and
efficiency. In particular, we first sample architectures from a joint
categorical distribution. Then the search space is dynamically pruned and its
distribution is updated every few epochs. With the proposed efficient network
generation method, we directly obtain the optimal neural architectures on given
constraints, which is practical for on-device models across diverse search
spaces and constraints. The architectures searched by our method achieve
remarkable top-1 accuracies, 97.56 and 77.2 on CIFAR-10 and ImageNet (mobile
settings), respectively, with the fastest search process, i.e., only 1.8 GPU
hours on a Tesla V100. Codes for searching and network generation are available
at: https://openi.pcl.ac.cn/PCL AutoML/XNAS.
