Domain adaptation

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Domain adaptation is a field associated with machine learning and transfer learning. It addresses the challenge of training a model on one data distribution (the source domain) and applying it to a related but different data distribution (the target domain).

A common example is spam filtering, where a model trained on emails from one user (source domain) is adapted to handle emails for another user with significantly different patterns (target domain).

Domain adaptation techniques can also leverage unrelated data sources to improve learning. When multiple source distributions are involved, the problem extends to multi-source domain adaptation.^[1]

Domain adaptation is a specialized area within transfer learning. In domain adaptation, the source and target domains share the same feature space but differ in their data distributions. In contrast, transfer learning encompasses broader scenarios, including cases where the target domain’s feature space differs from that of the source domain(s).^[2]