# Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies

arXiv: https://arxiv.org/abs/2506.24093 · HTML: https://mlsanity.com/paper/2506.24093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [How much real data do we actually need: Analyzing object detection performance using synthetic and real data](https://mlsanity.com/paper/1907.07061/) (2019)
- [Annotated History of Modern AI and Deep Learning](https://mlsanity.com/paper/2212.11279/) (2022)
- [Object Detection Using Deep CNNs Trained on Synthetic Images](https://mlsanity.com/paper/1706.06782/) (2017)
- [ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer](https://mlsanity.com/paper/2204.11891/) (2022)
- [Analysis of Training Object Detection Models with Synthetic Data](https://mlsanity.com/paper/2211.16066/) (2022)
- [Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data](https://mlsanity.com/paper/2202.00632/) (2022)

## Built on by (most cited first, 2 of 2)

- [Automated rock joint trace mapping using a supervised learning model trained on synthetic data generated by parametric modelling](https://mlsanity.com/paper/2602.07590/) (2026)
- [Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset](https://mlsanity.com/paper/2607.12165/) (2026)

## Abstract (arXiv)

Synthetic data has emerged as a cost-effective alternative to real data for training artificial neural networks (ANN). However, the disparity between synthetic and real data results in a domain gap. That gap leads to poor performance and generalization of the trained ANN when applied to real-world scenarios. Several strategies have been developed to bridge this gap, which combine synthetic and real data, known as mixed training using hybrid datasets. While these strategies have been shown to mitigate the domain gap, a systematic evaluation of their generalizability and robustness across various tasks and architectures remains underexplored. To address this challenge, our study comprehensively analyzes two widely used mixing strategies on three prevalent architectures and three distinct hybrid datasets. From these datasets, we sample subsets with varying proportions of synthetic to real data to investigate the impact of synthetic and real components. The findings of this paper provide valuable insights into optimizing the use of synthetic data in the training process of any ANN, contributing to enhancing robustness and efficacy.