Imbalanced Spectral Data Analysis Using Data Augmentation Based On The Generative Adversarial Network

This article discusses the use of deep neural networks (DNNs) and a novel data augmentation method based on generative adversarial networks (GANs) to improve the classification of imbalanced spectral data in materials science. The method has been shown to significantly improve classification performance compared to existing data augmentation methods.