# Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/1910.10563 · HTML: https://mlsanity.com/paper/1910.10563/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 30 of 32)

- [HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2204.13132/) (2022)
- [Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.11249/) (2021)
- [Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning](https://mlsanity.com/paper/2210.02326/) (2022)
- [FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation](https://mlsanity.com/paper/2204.01587/) (2022)
- [Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2209.07695/) (2022)
- [CoMoGAN: continuous model-guided image-to-image translation](https://mlsanity.com/paper/2103.06879/) (2021)
- [Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation](https://mlsanity.com/paper/2304.02991/) (2023)
- [Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2110.06685/) (2021)
- [Domain-robust VQA with diverse datasets and methods but no target labels](https://mlsanity.com/paper/2103.15974/) (2021)
- [Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2306.09098/) (2023)
- [Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2104.02633/) (2021)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2201.06974/) (2022)
- [Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework](https://mlsanity.com/paper/2411.12558/) (2024)
- [Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations](https://mlsanity.com/paper/2108.03021/) (2021)
- [Reciprocal Normalization for Domain Adaptation](https://mlsanity.com/paper/2112.10474/) (2021)
- [FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation](https://mlsanity.com/paper/2512.05140/) (2025)
- [Enabling the Network to Surf the Internet](https://mlsanity.com/paper/2102.12205/) (2021)
- [Semantically Consistent Image-to-Image Translation for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.03522/) (2021)
- [Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space](https://mlsanity.com/paper/2201.06415/) (2022)
- [Learning Good Features to Transfer Across Tasks and Domains](https://mlsanity.com/paper/2301.11310/) (2023)
- [MAC: ModAlity Calibration for Object Detection](https://mlsanity.com/paper/2310.09461/) (2023)
- [Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation](https://mlsanity.com/paper/2409.16706/) (2024)
- [Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization](https://mlsanity.com/paper/2208.02960/) (2022)
- [Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline](https://mlsanity.com/paper/2210.02991/) (2022)
- [Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation](https://mlsanity.com/paper/2510.19679/) (2025)
- [Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2302.01708/) (2023)
- [RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution](https://mlsanity.com/paper/2401.00460/) (2023)
- [FRIEREN: Federated Learning with Vision-Language Regularization for Segmentation](https://mlsanity.com/paper/2510.02114/) (2025)

## Abstract (arXiv)

Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits of rain as water drops, and this ultimately impacts the synthetic images realism. With our method, called domain bridge, web-crawled data are exploited to reduce the domain gap, leading to the inclusion of previously ignored elements in the generated images. We make use of a network for clear to rain translation trained with the domain bridge to extend our work to Unsupervised Domain Adaptation (UDA). In that context, we introduce an online multimodal style-sampling strategy, where image translation multimodality is exploited at training time to improve performances. Finally, a novel approach for self-supervised learning is presented, and used to further align the domains. With our contributions, we simultaneously increase the realism of the generated images, while reaching on par performances with respect to the UDA state-of-the-art, with a simpler approach.