More is Better: Deep Domain Adaptation with Multiple Sources
arxiv(2024)
摘要
In many practical applications, it is often difficult and expensive to obtain
large-scale labeled data to train state-of-the-art deep neural networks.
Therefore, transferring the learned knowledge from a separate, labeled source
domain to an unlabeled or sparsely labeled target domain becomes an appealing
alternative. However, direct transfer often results in significant performance
decay due to domain shift. Domain adaptation (DA) aims to address this problem
by aligning the distributions between the source and target domains.
Multi-source domain adaptation (MDA) is a powerful and practical extension in
which the labeled data may be collected from multiple sources with different
distributions. In this survey, we first define various MDA strategies. Then we
systematically summarize and compare modern MDA methods in the deep learning
era from different perspectives, followed by commonly used datasets and a brief
benchmark. Finally, we discuss future research directions for MDA that are
worth investigating.
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