Paper
Domain Generalization using Ensemble Learning
Published Mar 18, 2021 · Yusuf Mesbah, Youssef Youssry Ibrahim, Adil Khan
ArXiv
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7
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Influential Citations
Abstract
Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model’s weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.
Study Snapshot
Key takeawayEnsemble learning improves domain generalization by combining base deep learning models trained on a single source, achieving promising improvements over single source models.
PopulationOlder adults (50-71 years)
Sample size24
MethodsObservational
OutcomesBody Mass Index projections
ResultsSocial networks mitigate obesity in older groups.
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