计算机科学
可微函数
建筑
网络体系结构
搜索算法
梯度下降
正规化(语言学)
空格(标点符号)
编码(集合论)
理论计算机科学
数学优化
算法
计算机工程
人工神经网络
人工智能
数学
计算机网络
集合(抽象数据类型)
程序设计语言
视觉艺术
艺术
数学分析
操作系统
作者
Lan Huang,Shiqi Sun,Jia Zeng,Wencong Wang,Wei Pang,Kangping Wang
标识
DOI:10.1016/j.ins.2023.01.129
摘要
Differentiable architecture search (DARTS) is an effective neural architecture search algorithm based on gradient descent. However, there are two limitations in DARTS. First, a small proxy search space is exploited due to memory and computational resource constraints. Second, too many simple operations are preferred, which leads to the network deterioration. In this paper, we propose a uniform-space differentiable architecture search, named U-DARTS, to address the above problems. In one hand, the search space is redesigned to enable the search and evaluation of the architectures in the same space, and the new search space couples with a sampling and parameter sharing strategy to reduce resource overheads. This means that various cell structures are explored directly rather than cells with same structure are stacked to compose the network. In another hand, a regularization method, which takes the depth and the complexity of the operations into account, is proposed to prevent network deterioration. Our experiments show that U-DARTS is able to find excellent architectures. Specifically, we achieve an error rate of 2.59% with 3.3M parameters on CIFAR-10. The code is released in https://github.com/Sun-Shiqi/U-DARTS.
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