Generative Adversarial Networks for Data Augmentation

Abstract

Generative Adversarial Networks (GANs) have emerged as a powerful tool in the field of machine learning for generating synthetic data that closely resembles real data. This paper investigates the use of GANs for data augmentation, a technique that can enhance the performance and robustness of machine learning models. By generating additional training data, GANs address the challenge of limited labeled data, which is common in many machine learning tasks. We provide an overview of GANs and their training process, followed by a discussion on various strategies and architectures used for data augmentation. Furthermore, we review recent studies and applications of GANs in different domains, highlighting their impact on improving the performance of machine learning models. Through this paper, we aim to provide insights into the effectiveness of GANs for data augmentation and their potential to advance machine learning research.