Transfer Learning in Astronomy: A New Machine-Learning Paradigm

Understand

The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any task in need of a predictive model is looked in isolation, and ignores the existence of other similar tasks.

• In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks.
• The new emerging area is referred to as transfer learning.
• In this paper, I briefly describe the motivation behind the use of transfer learning techniques, and explain how such techniques can be used to solve popular problems in astronomy.