Domain adaptation is an important branch of transfer learning. It narrows the data distribution gap between the source domain (where training data resides) and the target domain (where actual inference data resides) through algorithms. It enables models trained on the source domain to maintain good performance in the target domain with differences in scenes, lighting, equipment, etc., solving the generalization problem in model deployment.
