18927

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao
Fujian Key Laboratory of Sensing and Computing for Smart City,Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, Xiamen, China
arXiv:1905.13543 [cs.CV], (28 May 2019)

@misc{zheng2019dynamic,

   title={Dynamic Distribution Pruning for Efficient Network Architecture Search},

   author={Xiawu Zheng and Rongrong Ji and Lang Tang and Yan Wan and Baochang Zhang and Yongjian Wu and Yunsheng Wu and Ling Shao},

   year={2019},

   eprint={1905.13543},

   archivePrefix={arXiv},

   primaryClass={cs.CV}

}

Network architectures obtained by Neural Architecture Search (NAS) have shown state-of-the-art performance in various computer vision tasks. Despite the exciting progress, the computational complexity of the forward-backward propagation and the search process makes it difficult to apply NAS in practice. In particular, most previous methods require thousands of GPU days for the search process to converge. In this paper, we propose a dynamic distribution pruning method towards extremely efficient NAS, which samples architectures from a joint categorical distribution. The search space is dynamically pruned every a few epochs to update this distribution, and the optimal neural architecture is obtained when there is only one structure remained. We conduct experiments on two widely-used datasets in NAS. On CIFAR-10, the optimal structure obtained by our method achieves the state-of-the-art 1.9% test error, while the search process is more than 1,000 times faster (only 1.5 GPU hours on a Tesla V100) than the state-of-the-art NAS algorithms. On ImageNet, our model achieves 75.2% top-1 accuracy under the MobileNet settings, with a time cost of only 2 GPU days that is 100% acceleration over the fastest NAS algorithm. The code is available.
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