Jabin Md Asaduzzaman, Fok Mable P
Opt Lett. 2022 Oct 15;47(20):5276-5279. doi: 10.1364/OL.471874.
In this Letter, an unsupervised-learning platform-generative adversarial network (GAN)-is proposed for experimental data augmentation in a deep-learning assisted photonic-based instantaneous microwave frequency measurement (IFM) system. Only 75 sets of experimental data are required and the GAN can augment the small amount of data into 5000 sets of data for training the deep learning model. Furthermore, frequency measurement error of the estimated frequency has improved by an order of magnitude from 50 MHz to 5 MHz. The proposed use of GAN effectively reduces the amount of experimental data needed by 98.75% and reduces measurement error by 10 times.
在本信函中，提出了一种无监督学习平台——生成对抗网络（GAN），用于基于深度学习的光子瞬时微波频率测量（IFM）系统中的实验数据增强。仅需75组实验数据，GAN就能将少量数据扩充为5000组数据，用于训练深度学习模型。此外，估计频率的频率测量误差从50 MHz提高了一个数量级至5 MHz。所提出的GAN的使用有效地将所需实验数据量减少了98.75%，并将测量误差降低了10倍。
