.png){kind=icon}
返回
An Efficient Multi-Objective Evolutionary Zero-Shot Neural Architecture Search Framework for Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1142/S0129065723500168.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Neural Architecture Search (NAS) has recently shown a powerful ability to engineer networks automatically on various tasks. Most current approaches navigate the search direction with the validation performance-based architecture evaluation methodology, which estimates an architecture's quality by training and validating on a specific large dataset. However, for small-scale datasets, the model's performance on the validation set cannot precisely estimate that on the test set. The imprecise architecture evaluation can mislead the search to sub-optima. To address the above problem, we propose an efficient multi-objective evolutionary zero-shot NAS framework by evaluating architectures with zero-cost metrics, which can be calculated with randomly initialized models in a training-free manner. Specifically, a general zero-cost metric design principle is proposed to unify the current metrics and help develop several new metrics. Then, we offer an efficient computational method for multi-zero-cost metrics by calculating them in one forward and backward pass. Finally, comprehensive experiments have been conducted on NAS-Bench-201 and MedMNIST. The results have shown that the proposed method can achieve sufficiently accurate, high-throughput performance on MedMNIST and 20xfaster than the previous best method.
Keyword:
Neural architecture search
image classification
zero-shot NAS
zero-cost metric
期刊
IF:
6.4
论文数:
1.2K
被引数:
3.3K
机构
引用论文
.png){kind=icon}
分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order通过按层次顺序动态修剪超网进行一次性神经体系结构搜索
PREAI
分享收藏
查看更多内容