Architecture performance evaluation is the most time-consuming part of neural architecture search (NAS). Zero-Shot NAS accelerates the evaluation by utilizing zero-cost proxies instead of training. Though effective, existing zero-cost proxies require invoking backpropagations or running networks on input data, making it difficult to further accelerate the computation of proxies. To alleviate this issue, architecture topologies are used to evaluate the performance of networks in this study. We prove that particular architectural topologies decrease the local entropy of feature maps, which degrades specific features to a bias, thereby reducing network performance. Based on this proof, architectural topologies are utilized to quantify the suppression of local entropy decrease (SED) as a data-free and running-free proxy. Experimental results show that SED outperforms most state-of-the-art proxies in terms of architecture selection on five benchmarks, with computation time reduced by three orders of magnitude. We further compare the SED-based NAS with state-of-the-art proxies. SED-based NAS selects the architecture with higher accuracy and fewer parameters in only one second. The theoretical analyses of local entropy and experimental results demonstrate that the suppression of local entropy decrease facilitates selecting optimal architectures in Zero-Shot NAS.

翻译：架构性能评估是神经架构搜索（NAS）中最耗时的环节。零样本NAS通过使用零成本代理替代训练来加速评估。尽管现有零成本代理方法有效，但仍需调用反向传播或基于输入数据运行网络，难以进一步加速代理计算。为缓解此问题，本研究利用架构拓扑结构评估网络性能。我们证明特定架构拓扑会降低特征图的局部熵，使特定特征退化为偏置，从而损害网络性能。基于此证明，我们利用架构拓扑量化局部熵减抑制（SED）作为无需数据和前向运行的代理指标。实验结果表明，在五个基准测试的架构选择任务中，SED在计算时间降低三个数量级的同时，性能优于多数现有先进代理方法。我们进一步将基于SED的NAS与前沿代理方法对比：基于SED的NAS仅需一秒即可选出精度更高、参数更少的架构。局部熵的理论分析与实验结果共同证明，局部熵减抑制机制能有效促进零样本NAS中的最优架构选择。