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Pseudo-Labeling Domain Adaptation Using Multi-Model Learning

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Autor(es):
Tomita, Victor Akihito Kamada ; Marcacini, Ricardo Marcondes
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: IEEE ACCESS; v. 13, p. 19-pg., 2025-01-01.
Resumo

With the constant growth of state-of-the-art models, obtaining sufficient labeled data to train these models for specific domains has become increasingly costly. Domain adaptation methods offer a potential solution to enhance model performance in new, unseen domains while minimizing the need for manual annotation of target domain. Despite recent advances in using pseudo-labeling for domain adaptation, significant challenges remain in maximizing the effectiveness of pseudo-labeling, particularly when aiming to create informative and interpretable representations from pseudo-labels. To address these challenges, we introduce the method Pseudo-labeling Domain Adaptation (PDA), which leverages pseudo-labels generated by multiple models to create a robust cross-domain representation. Additionally, to further mitigate the domain-shift problem, we propose a novel method called UMAP Domain Adaptation (UMAP DA), a UMAP-based technique that allows for connections only between nodes from different domains. We use these representations to construct a heterogeneous bipartite graph, where a neural network is employed for final classification. Experiments on six different datasets show an average F1-score improvement of 8 points, measuring the harmonic mean of precision and recall, compared to existing methods in the literature. The proposed method enhances both performance and interpretability, offering a new direction for cross-domain learning with pseudo-labels. (AU)

Processo FAPESP: 18/15163-6 - Um framework de aprendizagem experiencial para o ensino de gerenciamento de projetos de software
Beneficiário:Maria Lydia Fioravanti
Modalidade de apoio: Bolsas no Brasil - Doutorado
Processo FAPESP: 22/01793-3 - Avaliação de um framework de design de aprendizagem para ensino de gerenciamento de projetos de software usando engenharia de software empírica
Beneficiário:Maria Lydia Fioravanti
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado