correcting conditional and marginal distribution in transfer learning

I understand that in case of transfer learning, we can have the target and the source data having different domain distributions. In such cases, authors in many papers suggest bringing the marginal and conditional distributions of the target and the source closer, i.e, minimize the difference between the marginal and conditional distributions. Can someone please help me understand this by giving an intuitive explanation for this? I am unable to understand what exactly the author means when he says by bringing the distributions closer? Explanations using visual representations would be helpful.