Domain adaptation is a subfield of machine learning focused on techniques that enable models trained on data from a source domain to perform effectively on data from a related but statistically distinct target domain, thereby addressing the challenge of distribution shift. This research area investigates methodologies for transferring knowledge or aligning feature spaces between domains to improve model generalization and performance in scenarios where target domain data is scarce or unlabeled, mitigating the need for extensive target-specific annotation.