Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorithms inefficient. This work presents a novel architecture search algorithm, called GPT-NAS, that optimizes neural architectures by Generative Pre-Trained (GPT) model. In GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus could learn the fundamental law of building neural architectures. Therefore, GPT-NAS leverages the generative pre-trained (GPT) model to propose reasonable architecture components given the basic one. Such an approach can largely reduce the search space by introducing prior knowledge in the search process. Extensive experimental results show that our GPT-NAS method significantly outperforms seven manually designed neural architectures and thirteen architectures provided by competing NAS methods. In addition, our ablation study indicates that the proposed algorithm improves the performance of finely tuned neural architectures by up to about 12% compared to those without GPT, further demonstrating its effectiveness in searching neural architectures.

GPT-NAS是一种基于预训练模型的神经结构搜索算法，通过引入先验知识来大大减少搜索空间，从而优化神经网络架构，并在实验中取得了比其他七个手动设计的神经结构和竞争NAS方法提供的13个神经结构的好结果，并提高了经过精细调整的神经结构的性能。

GPT-NAS: 采用生成式预训练模型进行神经架构搜索