DARTSRepair: Core-failure-set guided DARTS for network robustness to common corruptions

Highlights：

• We focus on an important but challenging problem that is rarely explored, that is, how to enhance the robustness of deployed deep neural network (DNN) via the guidance of a few collected and misclassified examples that might containing unknown corruptions.

• We first conduct an empirical study and validate that the model architectures can be definitely related to the corruptions having a specific pattern.

• We propose a novel core-failure-set guided DARTS that embeds a Kcenter-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture.

• Compared with the state-of-the-art NAS and data-augmentation-based enhancement methods, our final method can achieve higher accuracy on all corrupted datasets and the original clean dataset. In particular, on some of the corruptions, we can achieve over 45% absolute accuracy improvements.