What is domain adaptation?

Domain adaptation is a machine learning technique that aims to apply a model trained on one or more source domains to a different but related target domain. It is commonly used when labeled data is scarce or unavailable in the target domain, but abundant in the source domain(s). The goal is to minimize the difference between the source and target domains, allowing the model to generalize effectively to the new domain. This can be achieved through various methods, including instance-based, feature-based, and parameter-based adaptation techniques.