Domain Adaptations for Computer Vision Applications

TL;DR

This paper surveys domain adaptation techniques in computer vision, addressing the challenge of applying models trained on one data distribution to different, unseen target domains, and discusses recent advances in the field.

Contribution

It provides a comprehensive overview of recent domain transfer learning methods specifically tailored for computer vision applications.

Findings

Summarizes key domain adaptation techniques in computer vision.

Highlights recent advances and trends in the field.

Identifies challenges and future directions for domain adaptation.

Abstract

A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn't hold in many applications. Instead, ample labeled data might exist in a particular `source' domain while inference is needed in another, `target' domain. Domain adaptation methods leverage labeled data from both domains to improve classification on unseen data in the target domain. In this work we survey domain transfer learning methods for various application domains with focus on recent work in Computer Vision.