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

Xuhong Ren, Jianlang Chen, Felix Juefei-Xu, Wanli Xue, Qing Guo, Lei Ma, Jianjun Zhao, Shengyong Chen

研究成果: ジャーナルへの寄稿学術誌査読

5 被引用数 (Scopus)


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., 103 examples) to the clean training dataset (e.g., 5.0×104 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.

ジャーナルPattern Recognition
出版ステータス出版済み - 11月 2022

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能


「DARTSRepair: Core-failure-set guided DARTS for network robustness to common corruptions」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。