correcting conditional and marginal distribution in transfer learning
Bringing the distributions closer means that we are trying to modify the source data usually by performing weights in the instances or in the features (or in both in hybrid algorithms) in order to make the weighted data more similar to the targer data. If we achieve that then we will be able to train a model considering also the source data, which usually has a larger size or it is well annotated in comparison with the target.