Neural Architecture Search (NAS) has emerged as a key tool in identifying optimal configurations of deep neural networks tailored to specific tasks. However, training and assessing numerous architectures introduces considerable computational overhead. One method to mitigating this is through performance predictors, which offer a means to estimate the potential of an architecture without exhaustive training. Given that neural architectures fundamentally resemble Directed Acyclic Graphs (DAGs), Graph Neural Networks (GNNs) become an apparent choice for such predictive tasks. Nevertheless, the scarcity of training data can impact the precision of GNN-based predictors. To address this, we introduce a novel GNN predictor for NAS. This predictor renders neural architectures into vector representations by combining both the conventional and inverse graph views. Additionally, we incorporate a customized training loss within the GNN predictor to ensure efficient utilization of both types of representations. We subsequently assessed our method through experiments on benchmark datasets including NAS-Bench-101, NAS-Bench-201, and the DARTS search space, with a training dataset ranging from 50 to 400 samples. Benchmarked against leading GNN predictors, the experimental results showcase a significant improvement in prediction accuracy, with a 3%--16% increase in Kendall-tau correlation. Source codes are available at https://github.com/EMI-Group/fr-nas.

翻译：神经架构搜索（NAS）已成为针对特定任务识别深度神经网络最优配置的关键工具。然而，训练和评估大量架构会引入可观的计算开销。缓解这一问题的方法之一是使用性能预测器，它能够在不进行完整训练的情况下估计架构的潜力。由于神经架构本质上类似于有向无环图（DAG），图神经网络（GNN）自然成为此类预测任务的理想选择。尽管如此，训练数据的稀缺性可能影响基于GNN的预测器的精度。为解决此问题，我们提出了一种用于NAS的新型GNN预测器。该预测器通过结合常规图视角与反向图视角，将神经架构转化为向量表示。此外，我们在GNN预测器中引入定制化训练损失，以确保两种表示形式的高效利用。我们随后在NAS-Bench-101、NAS-Bench-201和DARTS搜索空间等基准数据集上评估了该方法，训练数据规模从50到400个样本不等。与领先的GNN预测器相比，实验结果表明预测精度显著提升，Kendall-tau相关系数提高了3%至16%。源代码见https://github.com/EMI-Group/fr-nas。