Machine learning for synthetic data generation: a review

Y Lu, M Shen, H Wang, X Wang, C van Rechem… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Synthetic data generation: State of the art in health care domain

H Murtaza, M Ahmed, NF Khan, G Murtaza… - Computer Science …, 2023‏ - Elsevier
Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Generative adversarial networks: A survey toward private and secure applications

Z Cai, Z **ong, H Xu, P Wang, W Li, Y Pan - ACM Computing Surveys …, 2021‏ - dl.acm.org
Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

Generative adversarial networks (GANs) challenges, solutions, and future directions

D Saxena, J Cao - ACM Computing Surveys (CSUR), 2021‏ - dl.acm.org
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

T Chakraborty, UR KS, SM Naik, M Panja… - Machine Learning …, 2024‏ - iopscience.iop.org
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

Privacy-preserving blockchain-based federated learning for traffic flow prediction

Y Qi, MS Hossain, J Nie, X Li - Future Generation Computer Systems, 2021‏ - Elsevier
As accurate and timely traffic flow information is extremely important for traffic management,
traffic flow prediction has become a vital component of intelligent transportation systems …

Dense: Data-free one-shot federated learning

J Zhang, C Chen, B Li, L Lyu, S Wu… - Advances in …, 2022‏ - proceedings.neurips.cc
Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …

Dreamartist: Towards controllable one-shot text-to-image generation via positive-negative prompt-tuning

Z Dong, P Wei, L Lin - arxiv preprint arxiv:2211.11337, 2022‏ - arxiv.org
Large-scale text-to-image generation models have achieved remarkable progress in
synthesizing high-quality, feature-rich images with high resolution guided by texts. However …

Threats, attacks, and defenses in machine unlearning: A survey

Z Liu, H Ye, C Chen, Y Zheng… - IEEE Open Journal of the …, 2025‏ - ieeexplore.ieee.org
Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

FedDPGAN: federated differentially private generative adversarial networks framework for the detection of COVID-19 pneumonia

L Zhang, B Shen, A Barnawi, S **, N Kumar… - Information Systems …, 2021‏ - Springer
Existing deep learning technologies generally learn the features of chest X-ray data
generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia …