In prediction-based Neural Architecture Search (NAS), performance indicators derived from graph convolutional networks have shown remarkable success. These indicators, achieved by representing feed-forward structures as component graphs through one-hot encoding, face a limitation: their inability to evaluate architecture performance across varying search spaces. In contrast, handcrafted performance indicators (zero-shot NAS), which use the same architecture with random initialization, can generalize across multiple search spaces. Addressing this limitation, we propose a novel approach for zero-shot NAS using deep learning. Our method employs Fourier sum of sines encoding for convolutional kernels, enabling the construction of a computational feed-forward graph with a structure similar to the architecture under evaluation. These encodings are learnable and offer a comprehensive view of the architecture's topological information. An accompanying multi-layer perceptron (MLP) then ranks these architectures based on their encodings. Experimental results show that our approach surpasses previous methods using graph convolutional networks in terms of correlation on the NAS-Bench-201 dataset and exhibits a higher convergence rate. Moreover, our extracted feature representation trained on each NAS benchmark is transferable to other NAS benchmarks, showing promising generalizability across multiple search spaces. The code is available at: https://github.com/minh1409/DFT-NPZS-NAS

翻译：在基于预测的神经架构搜索（NAS）中，源自图卷积网络的性能指标已展现出卓越成效。这类指标通过将前馈结构以独热编码表示为组件图，但存在一个局限：无法评估架构在不同搜索空间中的性能。相比之下，采用随机初始化权重的同构架构的手工设计性能指标（零样本NAS）能够跨多个搜索空间泛化。针对这一局限，我们提出了一种基于深度学习的零样本NAS新方法。该方法采用傅里叶正弦和编码处理卷积核，从而构建与待评估架构结构相似的计算前馈图。这些可学习的编码能够全面呈现架构的拓扑信息，并配合多层感知机（MLP）根据编码对架构进行排序。实验结果表明，我们的方法在NAS-Bench-201数据集上的相关性优于先前基于图卷积网络的方法，且具有更高的收敛速率。此外，在单个NAS基准上训练提取的特征表示可迁移至其他NAS基准，展现出跨多个搜索空间的良好泛化能力。代码开源地址：https://github.com/minh1409/DFT-NPZS-NAS