Data Augmentation using GANs for Deep Learning-based Localization Systems

Abstract

Recently, deep learning-based localization systems have become one of the most promising techniques due to their accuracy in complex environments. However, these techniques require large amounts of data for training. Obtaining such data is usually a tedious and time-consuming process, which hinders their practical deployment. In this paper, we propose a data augmentation framework for deep learning-based localization systems. The basic idea is to use a conditional Generative Adversarial Network that is able to learn the complex structures in the original training data and then generate high-quality synthetic data that matches the original data distribution. Evaluation of the proposed data augmentation framework in a real testbed shows that our technique can increase the average localization accuracy by 22.2% compared to the case of not using data augmentation. This demonstrates the promise of the proposed framework for enhancing deep learning-based localization systems.