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]