most citedBalanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime

101 citations · 101 across the 1 of their papers we have counts for

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cs.LG2022★ 101 cited

Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime

Yuqing Gao, Pengyuan Zhai, Khalid M. Mosalam

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 defic…