 This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
<meaning-changed>  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption  while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
 This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice the target data often come from a different distribution than the source data , known as domain shift . Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.