论文标题
平衡的半监督生成对抗网络，用于低数据不平衡级别的损害评估
Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime
论文作者
论文摘要
近年来，应用深度学习（DL）评估结构性损害已在基于视觉的结构健康监测（SHM）中越来越受欢迎。但是，数据缺陷和类不平衡都阻碍了DL在SHM的实际应用中的广泛采用。常见的缓解策略包括转移学习，过度采样和采样不足，但是这些临时方法仅提供有限的性能提升，而这种绩效的提升因一种情况而异。在这项工作中，我们介绍了一种生成对抗网络（GAN）的一种变体，该变体称为平衡的半监督GAN（BSS-GAN）。它采用了半监督的学习概念，并在培训中应用平衡的批次抽样来解决低数据和不平衡的级别问题。在低数据不平衡的级别制度下进行了一系列有关混凝土破裂和剥落分类的计算机实验，计算能力有限。结果表明，与其他常规方法相比，BSS-GAN能够在召回和$F_β$得分方面获得更好的损害检测，这表明其最先进的性能。
In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the Generative Adversarial Network (GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and $F_β$ score than other conventional methods, indicating its state-of-the-art performance.
