# Data Augmentation Using GANs

arXiv: https://arxiv.org/abs/1904.09135 · HTML: https://mlsanity.com/paper/1904.09135/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 30 of 58)

- [Effective Data Augmentation With Diffusion Models](https://mlsanity.com/paper/2302.07944/) (2023)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [GAN-Supervised Dense Visual Alignment](https://mlsanity.com/paper/2112.05143/) (2021)
- [Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes](https://mlsanity.com/paper/2312.12112/) (2023)
- [State-of-the-Art in the Architecture, Methods and Applications of StyleGAN](https://mlsanity.com/paper/2202.14020/) (2022)
- [Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models](https://mlsanity.com/paper/2401.12978/) (2024)
- [OCT-GAN: Neural ODE-based Conditional Tabular GANs](https://mlsanity.com/paper/2105.14969/) (2021)
- [Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation](https://mlsanity.com/paper/2209.09841/) (2022)
- [CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images](https://mlsanity.com/paper/2308.12288/) (2023)
- [LARGE: Latent-Based Regression through GAN Semantics](https://mlsanity.com/paper/2107.11186/) (2021)
- [On the Fairness of Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/2103.00950/) (2021)
- [GANORCON: Are Generative Models Useful for Few-shot Segmentation?](https://mlsanity.com/paper/2112.00854/) (2021)
- [Adversarial Imitation Learning with Trajectorial Augmentation and Correction](https://mlsanity.com/paper/2103.13887/) (2021)
- [GenLabel: Mixup Relabeling using Generative Models](https://mlsanity.com/paper/2201.02354/) (2022)
- [RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping](https://mlsanity.com/paper/2207.10653/) (2022)
- [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://mlsanity.com/paper/2308.16041/) (2023)
- [Personalized Representation from Personalized Generation](https://mlsanity.com/paper/2412.16156/) (2024)
- [Synthetic Data Generation for Fraud Detection using GANs](https://mlsanity.com/paper/2109.12546/) (2021)
- [InvGAN: Invertible GANs](https://mlsanity.com/paper/2112.04598/) (2021)
- [DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation](https://mlsanity.com/paper/2309.07909/) (2023)
- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2109.01583/) (2021)
- [Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic](https://mlsanity.com/paper/2312.02803/) (2023)
- [DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2501.08333/) (2025)
- [FROTE: Feedback Rule-Driven Oversampling for Editing Models](https://mlsanity.com/paper/2201.01070/) (2022)
- [SynDiff-AD: Improving Semantic Segmentation and End-to-End Autonomous Driving with Synthetic Data from Latent Diffusion Models](https://mlsanity.com/paper/2411.16776/) (2024)
- [Overcoming limited battery data challenges: A coupled neural network approach](https://mlsanity.com/paper/2111.15348/) (2021)
- [Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow](https://mlsanity.com/paper/2206.01864/) (2022)
- [Semi-Supervised Learning for Mars Imagery Classification and Segmentation](https://mlsanity.com/paper/2206.02180/) (2022)

## Abstract (arXiv)

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained using the training data generated by the GAN reached the same, (and surprisingly sometimes better), accuracy and recall than a DT trained on the original data set.