# One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models

arXiv: https://arxiv.org/abs/2303.18080 · HTML: https://mlsanity.com/paper/2303.18080/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 50 of its 67 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Is synthetic data from generative models ready for image recognition?](https://mlsanity.com/paper/2210.07574/) (2022)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.14887/) (2021)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2101.10979/) (2021)
- [FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation](https://mlsanity.com/paper/1612.02649/) (2016)
- [DACS: Domain Adaptation via Cross-domain Mixed Sampling](https://mlsanity.com/paper/2007.08702/) (2020)
- [ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning](https://mlsanity.com/paper/2007.07936/) (2020)
- [Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://mlsanity.com/paper/2004.07703/) (2020)

## Built on by (most cited first, 30 of 35)

- [Text-image Alignment for Diffusion-based Perception](https://mlsanity.com/paper/2310.00031/) (2023)
- [Collaborating Foundation Models for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2312.09788/) (2023)
- [Unified Language-driven Zero-shot Domain Adaptation](https://mlsanity.com/paper/2404.07155/) (2024)
- [Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation](https://mlsanity.com/paper/2312.01850/) (2023)
- [Unsupervised Domain Adaptation via Domain-Adaptive Diffusion](https://mlsanity.com/paper/2308.13893/) (2023)
- [Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition](https://mlsanity.com/paper/2505.19694/) (2025)
- [CamDiff: Camouflage Image Augmentation via Diffusion Model](https://mlsanity.com/paper/2304.05469/) (2023)
- [ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation](https://mlsanity.com/paper/2408.02226/) (2024)
- [Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration](https://mlsanity.com/paper/2305.12799/) (2023)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation](https://mlsanity.com/paper/2401.09496/) (2024)
- [ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation](https://mlsanity.com/paper/2402.06446/) (2024)
- [Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation](https://mlsanity.com/paper/2505.16360/) (2025)
- [ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer](https://mlsanity.com/paper/2403.13652/) (2024)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation](https://mlsanity.com/paper/2402.04929/) (2024)
- [SIDA: Synthetic Image Driven Zero-shot Domain Adaptation](https://mlsanity.com/paper/2507.18632/) (2025)
- [Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation](https://mlsanity.com/paper/2508.03300/) (2025)
- [Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance](https://mlsanity.com/paper/2509.22635/) (2025)
- [Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation](https://mlsanity.com/paper/2510.25279/) (2025)
- [Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy](https://mlsanity.com/paper/2410.07460/) (2024)
- [MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation](https://mlsanity.com/paper/2508.21435/) (2025)
- [Optical Music Recognition for Real-World Manuscripts with Synthetic Data](https://mlsanity.com/paper/2606.09479/) (2026)
- [Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning](https://mlsanity.com/paper/2408.03353/) (2024)
- [CA-LoRA: Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation](https://mlsanity.com/paper/2503.22172/) (2025)
- [Masked Language Prompting for Generative Data Augmentation in Few-shot Fashion Style Recognition](https://mlsanity.com/paper/2504.19455/) (2025)
- [OT Score: An OT based Confidence Score for Prototype-Assisted Source Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2505.11669/) (2025)
- [FreeDNA: Endowing Domain Adaptation of Diffusion-Based Dense Prediction with Training-Free Domain Noise Alignment](https://mlsanity.com/paper/2506.22509/) (2025)
- [IELDG: Suppressing Domain-Specific Noise with Inverse Evolution Layers for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2508.19604/) (2025)
- [Vicinity-Guided Discriminative Latent Diffusion for Privacy-Preserving Domain Adaptation](https://mlsanity.com/paper/2510.00478/) (2025)

## Abstract (arXiv)

Adapting a segmentation model from a labeled source domain to a target domain, where a single unlabeled datum is available, is one the most challenging problems in domain adaptation and is otherwise known as one-shot unsupervised domain adaptation (OSUDA). Most of the prior works have addressed the problem by relying on style transfer techniques, where the source images are stylized to have the appearance of the target domain. Departing from the common notion of transferring only the target ``texture'' information, we leverage text-to-image diffusion models (e.g., Stable Diffusion) to generate a synthetic target dataset with photo-realistic images that not only faithfully depict the style of the target domain, but are also characterized by novel scenes in diverse contexts. The text interface in our method Data AugmenTation with diffUsion Models (DATUM) endows us with the possibility of guiding the generation of images towards desired semantic concepts while respecting the original spatial context of a single training image, which is not possible in existing OSUDA methods. Extensive experiments on standard benchmarks show that our DATUM surpasses the state-of-the-art OSUDA methods by up to +7.1%. The implementation is available at https://github.com/yasserben/DATUM