Zero-cost proxies are nowadays frequently studied and used to search for neural architectures. They show an impressive ability to predict the performance of architectures by making use of their untrained weights. These techniques allow for immense search speed-ups. So far the joint search for well-performing and robust architectures has received much less attention in the field of NAS. Therefore, the main focus of zero-cost proxies is the clean accuracy of architectures, whereas the model robustness should play an evenly important part. In this paper, we analyze the ability of common zero-cost proxies to serve as performance predictors for robustness in the popular NAS-Bench-201 search space. We are interested in the single prediction task for robustness and the joint multi-objective of clean and robust accuracy. We further analyze the feature importance of the proxies and show that predicting the robustness makes the prediction task from existing zero-cost proxies more challenging. As a result, the joint consideration of several proxies becomes necessary to predict a model's robustness while the clean accuracy can be regressed from a single such feature.

翻译：零成本代理如今被频繁研究和用于搜索神经架构，其利用未训练权重预测架构性能的能力令人印象深刻。这些技术可实现巨大的搜索加速。然而在神经架构搜索（NAS）领域，同步搜索高性能与鲁棒架构的研究相对较少。因此，零成本代理主要关注架构的干净准确率，而模型鲁棒性同样应占据重要地位。本文分析常见零成本代理在热门NAS-Bench-201搜索空间中作为鲁棒性性能预测器的能力。我们关注两类任务：鲁棒性的单一预测，以及干净准确率与鲁棒性的联合多目标优化。我们进一步分析代理的特征重要性，表明预测鲁棒性会使现有零成本代理的预测任务更具挑战性。最终，需联合考虑多个代理才能预测模型鲁棒性，而干净准确率仅需单一特征即可回归。