Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures. (arXiv:2207.05321v1 [cs.LG])

Deep neural networks have been found vulnerable to adversarial attacks, thus
raising potentially concerns in security-sensitive contexts. To address this
problem, recent research has investigated the adversarial robustness of deep
neural networks from the architectural point of view. However, searching for
architectures of deep neural networks is computationally expensive,
particularly when coupled with adversarial training process. To meet the above
challenge, this paper proposes a bi-fidelity multiobjective neural architecture
search approach. First, we formulate the NAS problem for enhancing adversarial …

arxiv bi fidelity lg neural architectures search