# Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation

arXiv: https://arxiv.org/abs/1807.09384 · HTML: https://mlsanity.com/paper/1807.09384/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 18 of 18)

- [Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2209.10510/) (2022)
- [Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing](https://mlsanity.com/paper/2110.15128/) (2021)
- [Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving](https://mlsanity.com/paper/2304.11928/) (2023)
- [Neuron Coverage-Guided Domain Generalization](https://mlsanity.com/paper/2103.00229/) (2021)
- [Towards Domain Invariant Single Image Dehazing](https://mlsanity.com/paper/2101.10449/) (2021)
- [Learning Instance-Specific Adaptation for Cross-Domain Segmentation](https://mlsanity.com/paper/2203.16530/) (2022)
- [Domain-robust VQA with diverse datasets and methods but no target labels](https://mlsanity.com/paper/2103.15974/) (2021)
- [Multiple Style Transfer via Variational AutoEncoder](https://mlsanity.com/paper/2110.07375/) (2021)
- [Graphical Modeling for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2104.13057/) (2021)
- [Condition-Invariant Semantic Segmentation](https://mlsanity.com/paper/2305.17349/) (2023)
- [Synthetic training data generation for deep learning based quality inspection](https://mlsanity.com/paper/2104.02980/) (2021)
- [Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss](https://mlsanity.com/paper/2208.08815/) (2022)
- [Domain Adaptation for Rare Classes Augmented with Synthetic Samples](https://mlsanity.com/paper/2110.12216/) (2021)
- [Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging](https://mlsanity.com/paper/2512.13855/) (2025)
- [Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving](https://mlsanity.com/paper/2509.13507/) (2025)
- [A Scalable Pipeline Combining Procedural 3D Graphics and Guided Diffusion for Photorealistic Synthetic Training Data Generation in White Button Mushroom Segmentation](https://mlsanity.com/paper/2512.08747/) (2025)
- [Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks](https://mlsanity.com/paper/2403.15370/) (2024)
- [ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather](https://mlsanity.com/paper/2609.27533/) (2026)

## Abstract (arXiv)

Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem. In this paper, we show that by applying a straightforward modification to an existing photorealistic style transfer algorithm, we achieve state-of-the-art synthetic-to-real domain adaptation results. We conduct extensive experimental validations on four synthetic-to-real tasks for semantic segmentation and object detection, and show that our approach exceeds the performance of any current state-of-the-art GAN-based image translation approach as measured by segmentation and object detection metrics. Furthermore we offer a distance based analysis of our method which shows a dramatic reduction in Frechet Inception distance between the source and target domains, offering a quantitative metric that demonstrates the effectiveness of our algorithm in bridging the synthetic-to-real gap.