# Learning the Roots of Visual Domain Shift

arXiv: https://arxiv.org/abs/1607.06144 · HTML: https://mlsanity.com/paper/1607.06144/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2016.

## Built on by (most cited first, 9 of 9)

- [SPA: A Graph Spectral Alignment Perspective for Domain Adaptation](https://mlsanity.com/paper/2310.17594/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Self-Ensembling GAN for Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2112.07999/) (2021)
- [Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization](https://mlsanity.com/paper/2204.09334/) (2022)
- [SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy](https://mlsanity.com/paper/2211.04658/) (2022)
- [Dispensed Transformer Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2110.14944/) (2021)
- [Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency](https://mlsanity.com/paper/2409.12450/) (2024)
- [SPA++: Generalized Graph Spectral Alignment for Versatile Domain Adaptation](https://mlsanity.com/paper/2508.05182/) (2025)
- [A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields](https://mlsanity.com/paper/2209.13202/) (2022)

## Abstract (arXiv)

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.