Use this link to cite:
http://hdl.handle.net/2183/40187 A Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation
Loading...
Identifiers
Publication date
Authors
Advisors
Other responsabilities
Journal Title
Bibliographic citation
Blanco-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_3
Type of academic work
Academic degree
Abstract
[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.
Description
This 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.
Conference Proceeding presented at: Intelligent Data Engineering and Automated Learning – IDEAL 2023, 24th International Conference, Évora, Portugal, November 22–24, 2023.
Editor version
Rights
© 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).