Domain adaptation

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Domain adaptation^[1][2][3] is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning a model from a source data distribution and applying that model on a different (but related) target data distribution. For instance, one of the tasks of the common spam filtering problem consists in adapting a model from one user (the source distribution) to a new user who receives significantly different emails (the target distribution). Domain adaptation has also been shown to be beneficial to learning unrelated sources.^[4] Note that, when more than one source distribution is available the problem is referred to as multi-source domain adaptation.^[5]