# A survey of synthetic data augmentation methods in computer vision

arXiv: https://arxiv.org/abs/2403.10075 · HTML: https://mlsanity.com/paper/2403.10075/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 211 of its 323 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 30 of 211)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)

## Built on by (most cited first, 30 of 34)

- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2411.18275/) (2024)
- [LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion](https://mlsanity.com/paper/2404.00292/) (2024)
- [Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis](https://mlsanity.com/paper/2508.05580/) (2025)
- [A survey of datasets for computer vision in agriculture](https://mlsanity.com/paper/2502.16950/) (2025)
- [OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer](https://mlsanity.com/paper/2604.24762/) (2026)
- [Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models](https://mlsanity.com/paper/2510.06209/) (2025)
- [Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation](https://mlsanity.com/paper/2409.20332/) (2024)
- [GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction](https://mlsanity.com/paper/2503.15564/) (2025)
- [Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking](https://mlsanity.com/paper/2509.12046/) (2025)
- [Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition](https://mlsanity.com/paper/2503.14513/) (2025)
- [Partitioned Neural Network Training via Synthetic Intermediate Labels](https://mlsanity.com/paper/2403.11204/) (2024)
- [SynGen-Vision: Synthetic Data Generation for training industrial vision models](https://mlsanity.com/paper/2509.04894/) (2025)
- [RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance](https://mlsanity.com/paper/2512.22974/) (2025)
- [Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation](https://mlsanity.com/paper/2601.10449/) (2026)
- [Does Synthetic Layered Design Data Benefit Layered Design Decomposition?](https://mlsanity.com/paper/2605.15167/) (2026)
- [Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments](https://mlsanity.com/paper/2406.04890/) (2024)
- [Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations](https://mlsanity.com/paper/2408.17311/) (2024)
- [Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis](https://mlsanity.com/paper/2411.01929/) (2024)
- [Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs](https://mlsanity.com/paper/2504.02163/) (2025)
- [Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation](https://mlsanity.com/paper/2508.09188/) (2025)
- [MInDI-3D: Iterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography](https://mlsanity.com/paper/2508.09616/) (2025)
- [AutoGeTS: Knowledge-based Automated Generation of Text Synthetics for Improving Text Classification](https://mlsanity.com/paper/2508.10000/) (2025)
- [HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation](https://mlsanity.com/paper/2508.14345/) (2025)
- [Data Augmentation Improves Machine Unlearning](https://mlsanity.com/paper/2508.18502/) (2025)
- [Synthetic-to-Real Object Detection using YOLOv11 and Domain Randomization Strategies](https://mlsanity.com/paper/2509.15045/) (2025)
- [Latent space analysis and generalization to out-of-distribution data](https://mlsanity.com/paper/2511.15010/) (2025)
- [Object-Centric Data Synthesis for Category-level Object Detection](https://mlsanity.com/paper/2511.23450/) (2025)
- [Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI](https://mlsanity.com/paper/2602.08809/) (2026)
- [Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques](https://mlsanity.com/paper/2603.05158/) (2026)

## Abstract (arXiv)

The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.