Efficient Search of Comprehensively Robust Neural Architectures via
  Multi-fidelity Evaluation
Jialiang Sun Wen Yao Tingsong Jiang Xiaoqian Chen
Neural architecture search (NAS) has emerged as one successful technique to
find robust deep neural network (DNN) architectures. However, most existing
robustness evaluations in NAS only consider $l_{\infty}$ norm-based adversarial
noises. In order to improve the robustness of DNN models against multiple types
of noises, it is necessary to consider a comprehensive evaluation in NAS for
robust architectures. But with the increasing number of types of robustness
evaluations, it also becomes more time-consuming to find comprehensively robust
architectures. To alleviate this problem, we propose a novel efficient search
of comprehensively robust neural architectures via multi-fidelity evaluation
(ES-CRNA-ME). Specifically, we first search for comprehensively robust
architectures under multiple types of evaluations using the
weight-sharing-based NAS method, including different $l_{p}$ norm attacks,
semantic adversarial attacks, and composite adversarial attacks. In addition,
we reduce the number of robustness evaluations by the correlation analysis,
which can incorporate similar evaluations and decrease the evaluation cost.
Finally, we propose a multi-fidelity online surrogate during optimization to
further decrease the search cost. On the basis of the surrogate constructed by
low-fidelity data, the online high-fidelity data is utilized to finetune the
surrogate. Experiments on CIFAR10 and CIFAR100 datasets show the effectiveness
of our proposed method.