基于神经网络结构搜索的轻量化网络构建

轻量化网络可解决深度神经网络参数较多、计算量较高、难以部署在计算能力有限的边缘设备上等问题.针对轻量化网络中常用的分组卷积的分组结构问题,文中提出基于神经网络结构搜索的轻量化网络.将不同分组的卷积单元作为搜索空间,使用神经网络结构搜索,得到网络的分组结构和整体架构.同时为了兼顾准确率与计算量,提出循环退火搜索策略,用于解决神经网络结构搜索的多目标优化问题.在数据集上的实验表明,文中网络识别准确率较高,时间复杂度和空间复杂度较低.

Lightweight Model Construction Based on Neural Architecture Search

The traditional deep neural network cannot be deployed on the edge devices with limited computing capacity due to numerous parameters and high computation. In this paper, a lightweight network based on neural architecture search is specially designed to solve this problem. Convolution units of different groups are regarded as search space, and neural architecture search is utilized to obtain both the group structure and the overall architecture of the network. In the meanwhile, a cycle annealing search strategy is put forward to solve the multi-objective optimization problem of neural architecture search with the consideration of the accuracy and the computation cost of the model. Experiments on datasets show that the proposed network model achieves a better performance than the state-of-the-art methods.