A General Approach to Domain Adaptation with Applications in Astronomy

Understand

The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications.

• The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario.
• An example is to build a predictive model on spectroscopic data to identify Supernovae IA, while subsequently trying to adapt such model on photometric data.
• In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions.