Domain Generalization Using Category Information Independent of Domain Differences

Reiji Saito, Kazuhiro Hotta

2025

Abstract

Domain generalization is a technique aimed at enabling models to maintain high accuracy when applied to new environments or datasets (unseen domains) that differ from the datasets used in training. Generally, the accuracy of models trained on a specific dataset (source domain) often decreases significantly when evaluated on different datasets (target domain). This issue arises due to differences in domains caused by varying environmental conditions such as imaging equipment and staining methods. Therefore, we undertook two initiatives to perform segmentation that does not depend on domain differences. We propose a method that separates category information independent of domain differences from the information specific to the source domain. By using information independent of domain differences, our method enables learning the segmentation targets (e.g., blood vessels and cell nuclei). Although we extract independent information of domain differences, this cannot completely bridge the domain gap between training and test data. Therefore, we absorb the domain gap using the quantum vectors in Stochastically Quantized Variational AutoEncoder (SQ-VAE). In experiments, we evaluated our method on datasets for vascular segmentation and cell nucleus segmentation. Our methods improved the accuracy compared to conventional methods.

Paper Citation

in Harvard Style

Saito R. and Hotta K. (2025). Domain Generalization Using Category Information Independent of Domain Differences. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 368-376. DOI: 10.5220/0013300300003905

in Bibtex Style

@conference{icpram25,
author={Reiji Saito and Kazuhiro Hotta},
title={Domain Generalization Using Category Information Independent of Domain Differences},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={368-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013300300003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Domain Generalization Using Category Information Independent of Domain Differences
SN - 978-989-758-730-6
AU - Saito R.
AU - Hotta K.
PY - 2025
SP - 368
EP - 376
DO - 10.5220/0013300300003905
PB - SciTePress