  Network architecture search (NAS), in particular the differentiable
architecture search (DARTS) method, has shown a great power to learn excellent
model architectures on the specific dataset of interest. In contrast to using a
fixed dataset, in this work, we focus on a different but important scenario for
NAS: how to refine a deployed network's model architecture to enhance its
robustness with the guidance of a few collected and misclassified examples that
are degraded by some real-world unknown corruptions having a specific pattern
(e.g., noise, blur, etc.). To this end, we first conduct an empirical study to
validate that the model architectures can be definitely related to the
corruption patterns. Surprisingly, by just adding a few corrupted and
misclassified examples (e.g., $10^3$ examples) to the clean training dataset
(e.g., $5.0 \times 10^4$ examples), we can refine the model architecture and
enhance the robustness significantly. To make it more practical, the key
problem, i.e., how to select the proper failure examples for the effective NAS
guidance, should be carefully investigated. Then, we propose a novel
core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS
to select suitable corrupted failure examples to refine the model architecture.
We use our method for DARTS-refined DNNs on the clean as well as 15 corruptions
with the guidance of four specific real-world corruptions. Compared with the
state-of-the-art NAS as well as data-augmentation-based enhancement methods,
our final method can achieve higher accuracy on both corrupted datasets and the
original clean dataset. On some of the corruption patterns, we can achieve as
high as over 45% absolute accuracy improvements.
