This paper is published in Volume-10, Issue-1, 2024
Area
Machine Learning
Author
Keerthana V, Dr. S. Boopathi Raja
Org/Univ
Jain University, Bengaluru, Karnataka, India
Pub. Date
27 March, 2024
Paper ID
V10I1-1234
Publisher
Keywords
Generative Adversarial Networks (GANs), Deep learning, Synthetic data generation, Data scarcity

Citationsacebook

IEEE
Keerthana V, Dr. S. Boopathi Raja. GANs for Synthetic Data Generation: Advancements and Challenges using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Keerthana V, Dr. S. Boopathi Raja (2024). GANs for Synthetic Data Generation: Advancements and Challenges using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 10(1) www.IJARIIT.com.

MLA
Keerthana V, Dr. S. Boopathi Raja. "GANs for Synthetic Data Generation: Advancements and Challenges using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 10.1 (2024). www.IJARIIT.com.

Abstract

Generative Adversarial Networks (GANs) have emerged as a powerful tool for addressing data scarcity and privacy concerns in various domains such as healthcare, finance, and security. This paper provides a comprehensive overview of GANs and their applications in synthetic data generation. We discuss the importance of synthetic data, the challenges associated with its generation, and techniques for improving GAN performance. Through case studies and experiments, we demonstrate the effectiveness of GANs in generating realistic and diverse synthetic data. We also examine ethical considerations surrounding the use of synthetic data and outline future directions and challenges in this rapidly evolving field. Overall, this paper highlights the potential of GANs to revolutionize data generation and addresses key considerations for their responsible deployment in sensitive domains.