Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

In machine learning, generative modeling aims to learn to generate new data statistically
similar to the training data distribution. In this paper, we survey learning generative models …

Deep generative models (DGMs) are data-eager because learning a complex model on
limited data suffers from a large variance and easily overfits. Inspired by the classical …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond, promising a future where datasets can be tailored to individual …

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 …

While deep generative networks can simulate from complex data distributions, their utility
can be hindered by limitations on the data available for training. Specifically, the training …

Recent deep generative models (DGMs) such as generative adversarial networks (GANs)
and diffusion probabilistic models (DPMs) have shown their impressive ability in generating …

The performance of deep learning models is intrinsically tied to the quality volume and
relevance of their training data. Gathering ample data for production scenarios often …