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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 …
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
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 …
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
Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …
computer vision and natural language processing, among others, due to its generative …
Generative adversarial networks (GANs) challenges, solutions, and future directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …
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 …
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 …
traffic flow prediction has become a vital component of intelligent transportation systems …
Dense: Data-free one-shot federated learning
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 …
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
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 …
synthesizing high-quality, feature-rich images with high resolution guided by texts. However …
Threats, attacks, and defenses in machine unlearning: A survey
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 …
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
Existing deep learning technologies generally learn the features of chest X-ray data
generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia …
generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia …