# Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing

arXiv: https://arxiv.org/abs/2212.03826 · HTML: https://mlsanity.com/paper/2212.03826/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/1905.03198/) (2019)
- [ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks](https://mlsanity.com/paper/1907.12859/) (2019)
- [Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2112.04665/) (2021)
- [Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation](https://mlsanity.com/paper/2203.01539/) (2022)
- [Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection](https://mlsanity.com/paper/2006.05923/) (2020)
- [Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator](https://mlsanity.com/paper/2103.15502/) (2021)

## Built on by (most cited first, 3 of 3)

- [Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation](https://mlsanity.com/paper/2409.16706/) (2024)
- [OT on the Map: Quantifying Domain Shifts in Geographic Space](https://mlsanity.com/paper/2604.16220/) (2026)
- [Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications](https://mlsanity.com/paper/2404.11243/) (2024)

## Abstract (arXiv)

Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks, have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images, that i) leads to semantically consistent and noise-free images, ii) operates with a single target domain sample (i.e. one-shot) and iii) at a fraction of the number of parameters required from state-of-the-art methods. More specifically an image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains, and a perceptual network module and loss function is further introduced to enforce semantic consistency. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods. Our source code will be available at urlhttps://github.com/Sarmadfismael/LRMI2I.