Neural architecture search (NAS) enables the automatic design of neural
network models. However, training the candidates generated by the search
algorithm for performance evaluation incurs considerable computational
overhead. Our method, dubbed nasgraph, remarkably reduces the computational
costs by converting neural architectures to graphs and using the average
degree, a graph measure, as the proxy in lieu of the evaluation metric. Our
training-free NAS method is data-agnostic and light-weight. It can find the
best architecture among 200 randomly sampled architectures from NAS-Bench201 in
217 CPU seconds. Besides, our method is able to achieve competitive performance
on various datasets including NASBench-101, NASBench-201, and NDS search
spaces. We also demonstrate that nasgraph generalizes to more challenging tasks
on Micro TransNAS-Bench-101.

通过将神经结构转换为图形并使用平均度量作为代理评估指标，我们的 nasgraph 方法显著降低了训练自动设计的神经网络模型候选的计算成本，实现在 217 CPU 秒内从 NAS-Bench201 的 200 个随机采样架构中找到最佳架构，并在不同数据集上实现有竞争力的性能，包括 NASBench-101、NASBench-201 和 NDS 搜索空间。我们还展示了 nasgraph 在 Micro TransNAS-Bench-101 上可以推广至更具挑战性的任务。