Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

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 …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Modeling the probability distribution of rows in tabular data and generating realistic synthetic
data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous …

Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …