Blanco-Mallo, EvaBolón-Canedo, VerónicaRemeseiro, Beatriz2024-11-192024-11-192023-11-15Blanco-Mallo, E., Bolón-Canedo, V., Remeseiro, B. (2023). A Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_3978-3-031-48231-1 (print)978-3-031-48232-8 (online)0302-9743http://hdl.handle.net/2183/40187This version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-science/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-48232-8_3.Conference Proceeding presented at: Intelligent Data Engineering and Automated Learning – IDEAL 2023, 24th International Conference, Évora, Portugal, November 22–24, 2023.[Abstract]: Unsupervised domain adaptation focuses on reusing a model trained on a source domain in an unlabeled target domain. Two main approaches stand out in the literature: adversarial training for generating invariant features and minimizing the discrepancy between feature distributions. This paper presents a hybrid approach that combines these two methods with pseudo-labeling. The proposed loss function enhances the invariance between the features across domains and further defines the inter-class differences of the target set. Using a well-known dataset, Office31, we demonstrate that our proposal improves the overall performance, especially when the gap between domains is more significant.eng© 2023 Springer Nature Switzerland AG. Subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-science/policies/accepted-manuscript-terms).Unsupervised domain adaptationCertainty-aware pseudo- labelingMaximum mean discrepancyconference outputopen access10.1007/978-3-031-48232-8_3