Multiple Domain Generalization Using Category Information Independent of Domain Differences
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 difference...
| Authors | Reiji Saito, Kazuhiro Hotta |
|---|---|
| Primary category | cs.CV |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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