A General Approach to Domain Adaptation with Applications in Astronomy
This work addresses domain adaptation for astronomical applications, offering a novel approach that is incremental in its method but provides practical benefits in accuracy and efficiency.
The authors tackled domain adaptation in astronomy by proposing a method that assumes similar model complexity across domains and uses active learning, achieving increased accuracy and substantial computational cost savings in Supernova Ia classification and Mars landform identification.
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. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation. Results using two real astronomical problems, Supernova Ia classification and identification of Mars landforms, show two main advantages with our approach: increased accuracy performance and substantial savings in computational cost.