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<p>In recent years, Neural Architecture Search (NAS) has garnered widespread attention in the field of deep learning due to its significant potential in automating the construction of deep models. However, existing NAS methods primarily focus on optimizing network architecture, utilizing search strategies to find a high-performing network architecture within the search space as effectively as possible. And this process often requires repetitive and continuous searching and evaluation. With the significant advancements in Artificial Intelligence Generated Content (AIGC), an increasing number of researchers are utilizing deep generative models to create graph data. Neural network architecture can be viewed as Directed Acyclic Graphs(DAG) with labeled nodes. Therefore, we propose a graph generation model based on Graph Convolutional Network (GCN) and Generative Adversarial Network(GAN) to generate network architecture. With the aim of avoiding the repetitive and continuous searching and evaluation process in NAS. The CNN architecture generated by our algorithm in this paper achieves an accuracy of 94.37% on the CIFAR-10 dataset. While it may not outperform many other CNN models in terms of performance, it doesn’t require any expert knowledge and is generated automatically by the model, avoiding the need for repetitive searching and evaluation.</p>

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A Graph Generation Model for Convolutional Neural Network Architecture based on GCN and GAN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

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  • Changwei Song,
  • Yongjie Ma

摘要

In recent years, Neural Architecture Search (NAS) has garnered widespread attention in the field of deep learning due to its significant potential in automating the construction of deep models. However, existing NAS methods primarily focus on optimizing network architecture, utilizing search strategies to find a high-performing network architecture within the search space as effectively as possible. And this process often requires repetitive and continuous searching and evaluation. With the significant advancements in Artificial Intelligence Generated Content (AIGC), an increasing number of researchers are utilizing deep generative models to create graph data. Neural network architecture can be viewed as Directed Acyclic Graphs(DAG) with labeled nodes. Therefore, we propose a graph generation model based on Graph Convolutional Network (GCN) and Generative Adversarial Network(GAN) to generate network architecture. With the aim of avoiding the repetitive and continuous searching and evaluation process in NAS. The CNN architecture generated by our algorithm in this paper achieves an accuracy of 94.37% on the CIFAR-10 dataset. While it may not outperform many other CNN models in terms of performance, it doesn’t require any expert knowledge and is generated automatically by the model, avoiding the need for repetitive searching and evaluation.