In prediction-based Neural Architecture Search (NAS), performance indicators derived from graph convolutional networks have shown significant 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中，源自图卷积网络的性能指标已展现出显著成效。这些指标通过将前馈结构表示为独热编码的组件图来实现，但面临一个局限：无法评估跨不同搜索空间的架构性能。相比之下，采用相同架构随机初始化的手工性能指标（零样本架构搜索）能够泛化到多个搜索空间。针对这一局限，我们提出了一种利用深度学习进行零样本架构搜索的新方法。该方法采用傅里叶正弦和编码处理卷积核，从而构建一个与待评估架构结构类似的计算前馈图。这些编码是可学习的，并能全面反映架构的拓扑信息。随后，一个配套的多层感知机基于这些编码对架构进行排序。实验结果表明，在NAS-Bench-201数据集上，我们的方法在相关性方面优于以往使用图卷积网络的方法，并展现出更高的收敛速度。此外，我们在每个NAS基准数据集上训练得到的特征表示可迁移至其他NAS基准数据集，在多个搜索空间中展现出良好的泛化能力。代码可在以下网址获取：https://github.com/minh1409/DFT-NPZS-NAS