Pseudo-Labeling Domain Adaptation Using Multi-Model Learning

Pseudo-Labeling Domain Adaptation Using Multi-Model Learning

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 man...

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Bibliographic Details
Main Authors: Victor Akihito Kamada Tomita, Ricardo Marcondes Marcacini
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Domain adaptation
graph neural networks
interpretability
pseudo-labeling
Online Access:https://ieeexplore.ieee.org/document/10909469/
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https://ieeexplore.ieee.org/document/10909469/

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