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Multi-Source Domain Adaptation Using Approximate Label Matching

Domain adaptation, and transfer learning more generally, seeks to remedy the problem created when training and testing datasets are generated by different distributions. In this work, we introduce a new unsupervised domain adaptation algorithm for when there are multiple sources available to a learner. Our technique assigns a rough labeling on the target samples, then uses it to learn a transformation that aligns the two datasets before final classification. In this article we give a convenient implementation of our method, show several experiments using it, and compare it to other methods commonly used in the field.