Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation. (arXiv:2305.07308v1 [cs.CV])

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 …

architecture architectures arxiv deep neural network dnn evaluation fidelity multiple nas network neural architectures neural architecture search neural network robustness search types