# GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1810.10863 · HTML: https://mlsanity.com/paper/1810.10863/

## Key facts

- Cited by 107 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2018.

## Built on by (most cited first, 30 of 107)

- [UniScene: Unified Occupancy-centric Driving Scene Generation](https://mlsanity.com/paper/2412.05435/) (2024)
- [EnvEdit: Environment Editing for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.15685/) (2022)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Synthesizing Informative Training Samples with GAN](https://mlsanity.com/paper/2204.07513/) (2022)
- [Vision-Language Navigation with Random Environmental Mixup](https://mlsanity.com/paper/2106.07876/) (2021)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation](https://mlsanity.com/paper/2306.04607/) (2023)
- [Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation](https://mlsanity.com/paper/2109.07407/) (2021)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification](https://mlsanity.com/paper/2111.06399/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception](https://mlsanity.com/paper/2403.13304/) (2024)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [OCT-GAN: Neural ODE-based Conditional Tabular GANs](https://mlsanity.com/paper/2105.14969/) (2021)
- [Improving Fractal Pre-training](https://mlsanity.com/paper/2110.03091/) (2021)
- [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024)
- [Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification](https://mlsanity.com/paper/2112.07928/) (2021)
- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
- [You Only Cut Once: Boosting Data Augmentation with a Single Cut](https://mlsanity.com/paper/2201.12078/) (2022)
- [Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards](https://mlsanity.com/paper/2112.09327/) (2021)
- [Data-Centric Learning from Unlabeled Graphs with Diffusion Model](https://mlsanity.com/paper/2303.10108/) (2023)
- [Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models](https://mlsanity.com/paper/2306.02986/) (2023)
- [DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection](https://mlsanity.com/paper/2206.09592/) (2022)
- [Fine-grained Recognition with Learnable Semantic Data Augmentation](https://mlsanity.com/paper/2309.00399/) (2023)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models](https://mlsanity.com/paper/2108.01644/) (2021)
- [Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2202.06464/) (2022)
- [Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage](https://mlsanity.com/paper/2308.07151/) (2023)
- [Adversarial Imitation Learning with Trajectorial Augmentation and Correction](https://mlsanity.com/paper/2103.13887/) (2021)

## Abstract (arXiv)

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available.