# Generative Adversarial Networks for Data Augmentation

arXiv: https://arxiv.org/abs/2306.02019 · HTML: https://mlsanity.com/paper/2306.02019/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://mlsanity.com/paper/1810.10863/) (2018)
- [Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection](https://mlsanity.com/paper/1905.13456/) (2019)
- [Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network](https://mlsanity.com/paper/1910.11960/) (2019)
- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)

## Built on by (most cited first, 7 of 7)

- [Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes](https://mlsanity.com/paper/2312.12112/) (2023)
- [Evaluating the Impact of Data Augmentation on Predictive Model Performance](https://mlsanity.com/paper/2412.02108/) (2024)
- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2307.04771/) (2023)
- [Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey](https://mlsanity.com/paper/2406.16965/) (2024)
- [Trustworthy XAI and Application](https://mlsanity.com/paper/2410.17139/) (2024)
- [Principles and Components of Federated Learning Architectures](https://mlsanity.com/paper/2502.05273/) (2025)
- [A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Images](https://mlsanity.com/paper/2505.08517/) (2025)

## Abstract (arXiv)

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.