Target-Free Domain Adaptation through Cross-Adaptation (Student Abstract) - Obuchowski, Aleksander; Klaudel, Barbara; Frąckowski, Piotr; Krajna, Sebastian; Badyra, Wasyl; Czubenko, Michał; Kowalczuk, Zdzisław (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
The population characteristics of the datasets related to the same task may vary significantly and merging them may harm performance. In this paper, we propose a novel method of domain adaptation called "cross-adaptation". It allows for implicit adaptation to the target domain without the need for any labeled examples across this domain. We test our approach on 9 datasets for SARS-CoV-2 detection from complete blood count from different hospitals around the world. Results show that our solution is universal with respect to various classification algorithms and allows for up to a 10pp increase in F1 score on average.