# A General Approach to Domain Adaptation with Applications in Astronomy

## TL;DR

This paper introduces a novel domain adaptation method for astronomical data that does not depend on distribution proximity, leveraging model complexity similarity and active learning to improve accuracy and reduce computational costs.

## Contribution

The authors propose a new domain adaptation framework based on model complexity similarity and active learning, with a theoretical likelihood formulation for improved transfer learning in astronomy.

## Key findings

- Enhanced accuracy in astronomical classification tasks
- Significant reduction in computational costs
- Effective transfer learning without distribution proximity constraints

## Abstract

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.