Neural architecture search algorithms based on a recursive structure

doi:10.3969/j.issn.1000-5641.2022.04.004

Abstract

Abstract:

Neural architecture search algorithms aim to find more efficient neural network structures in a huge neural network structure space using computer heuristic search instead of manual search. Previous studies have addressed the problem of inefficient and time-consuming search for early neural network structures by introducing various constraints on the search space. While constraints on the search space can improve and stabilize the performance of the model, they ignore potentially efficient model structures. Hence, in this study, we constructed a recursive model search space that focuses more on the macroscopic structure of neural networks. We proposed a neural architecture search algorithm that explores this search space through a step-by-step incremental search approach. Experiments showed that the algorithm can efficiently perform neural architecture search tasks in complex search spaces, but still fell slightly short of the latest constrained search space-based neural architecture search algorithms.

Key words: neural architecture search, image classification, convolutional neural network

CLC Number:

TP391.4

Jizhou LI, Xin LIN. Neural architecture search algorithms based on a recursive structure[J]. Journal of East China Normal University(Natural Science), 2022, 2022(4): 31-42.

Figures/Tables 6

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Table 1

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References 23

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方法	CIFAR-10错误率/%	CIFAR-100错误率/%	参数量	搜索时间/(GPU days)
ResNet-110	4.61		1.70 M
DenseNet-BC	3.46	17.33	25.60 M
DenseNet-Cutout	2.56		26.20 M
NAS^[1]	4.47		7.10 M	22400.00
ENAS + macro search space^[4]	4.23		21.30 M	0.32
one-shot small (F=64)^[3]	3.90		5.00 M	3.30
Hierarchical^[8]	3.63		61.30 M	300.00
NASNet-A + Cutout^[2]	2.65		3.30 M	1800.00
AmoebaNet-A (N=6, F=36)^[11]	3.34	18.93	3.20 M	3150.00
ENAS + micro search space + Cutout^[4]	2.89		4.60 M	0.50
NAONet-WS + Cutout^[15]	2.93		2.50 M	0.30
DARTS (first order) + Cutout^[18]	3.00	17.76	3.30 M	1.50
DARTS (second order) + Cutout^[18]	2.77	17.54	3.30 M	4.00
PC-DARTS^[20]	2.57		3.60 M	0.10
Fair DARTS^[19]	2.54		3.32 M	0.40
P-DARTS^[21]	2.50	16.55	3.40 M	0.30
本文方案 (2 次扩张, 55 通道)	2.97	18.10	3.07 M	0.60
本文方案 (3 次扩张, 40 通道)	2.74	17.64	3.63 M	0.80
本文方案 (DARTS, 3 次扩张, 32 通道)	2.28	15.31	3.50 M	1.40

方案	准确率/%
在完整空间中随机采样 (10 次)	94.36 (±0.30)
第一步使用演化搜索, 第二步使用随机采样 (10 次)	94.48 (±0.25)
第一步使用随机采样, 第二步使用演化搜索 (10 次)	94.55 (±0.21)
直接搜索方案 (3 次)	94.66 (±0.11)
分步搜索方案 (3 次)	94.80 (±0.07)

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