# Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches

arXiv: https://arxiv.org/abs/2007.08781 · HTML: https://mlsanity.com/paper/2007.08781/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Scaling laws for learning with real and surrogate data](https://mlsanity.com/paper/2402.04376/) (2024)
- [Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata](https://mlsanity.com/paper/2209.00128/) (2022)
- [Evaluating a Synthetic Image Dataset Generated with Stable Diffusion](https://mlsanity.com/paper/2211.01777/) (2022)
- [Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level](https://mlsanity.com/paper/2303.01899/) (2023)
- [Robust Roadside Perception: an Automated Data Synthesis Pipeline Minimizing Human Annotation](https://mlsanity.com/paper/2306.17302/) (2023)
- [Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization](https://mlsanity.com/paper/2205.10368/) (2022)
- [Beyond Real Data: Synthetic Data through the Lens of Regularization](https://mlsanity.com/paper/2510.08095/) (2025)
- [TAEGAN: Generating Synthetic Tabular Data For Data Augmentation](https://mlsanity.com/paper/2410.01933/) (2024)
- [Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data](https://mlsanity.com/paper/2311.05371/) (2023)
- [Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs](https://mlsanity.com/paper/2401.07370/) (2024)
- [Using Images from a Video Game to Improve the Detection of Truck Axles](https://mlsanity.com/paper/2509.25644/) (2025)
- [ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection](https://mlsanity.com/paper/2607.27065/) (2026)

## Abstract (arXiv)

Deep neural networks have gained tremendous importance in many computer vision tasks. However, their power comes at the cost of large amounts of annotated data required for supervised training. In this work we review and compare different techniques available in the literature to improve training results without acquiring additional annotated real-world data. This goal is mostly achieved by applying annotation-preserving transformations to existing data or by synthetically creating more data.