  Recent neural architecture search (NAS) frameworks have been successful in
finding optimal architectures for given conditions (e.g., performance or
latency). However, they search for optimal architectures in terms of their
performance on clean images only, while robustness against various types of
perturbations or corruptions is crucial in practice. Although there exist
several robust NAS frameworks that tackle this issue by integrating adversarial
training into one-shot NAS, however, they are limited in that they only
consider robustness against adversarial attacks and require significant
computational resources to discover optimal architectures for a single task,
which makes them impractical in real-world scenarios. To address these
challenges, we propose a novel lightweight robust zero-cost proxy that
considers the consistency across features, parameters, and gradients of both
clean and perturbed images at the initialization state. Our approach
facilitates an efficient and rapid search for neural architectures capable of
learning generalizable features that exhibit robustness across diverse
perturbations. The experimental results demonstrate that our proxy can rapidly
and efficiently search for neural architectures that are consistently robust
against various perturbations on multiple benchmark datasets and diverse search
spaces, largely outperforming existing clean zero-shot NAS and robust NAS with
reduced search cost.
