DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions

TL;DR

This paper introduces DARTSRepair, a method that refines neural network architectures using a core-failure-set guided DARTS approach to significantly improve robustness against common corruptions with minimal corrupted data.

Contribution

The paper proposes a novel core-failure-set guided DARTS method that effectively selects corrupted failure examples for architecture refinement, enhancing robustness with limited corrupted data.

Findings

Refined architectures show significant robustness improvements on corrupted datasets.

Adding a small number of corrupted examples can substantially enhance model robustness.

Our method outperforms state-of-the-art NAS and data augmentation techniques.

Abstract

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.