Secure Domain Adaptation with Multiple Sources
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Multi-source unsupervised domain adaptation (MUDA) is a recently explored learning framework, where the goal is to address the challenge of labeled data scarcity in a target domain via transferring knowledge from multiple source domains with annotated data. Since the source data is distributed, the privacy of source domains' data can be a natural concern. We benefit from the idea of domain alignment in an embedding space to address the privacy concern for MUDA. Our method is based on aligning the sources and target distributions indirectly via internally learned distributions, without communicating data samples between domains. We justify our approach theoretically and perform extensive experiments to demonstrate that our method is effective and compares favorably against existing methods.
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