Abstract

Domain adaptation algorithms address the issue of transferring learning across computational models to adapt them to data from different distributions. In recent years, research in domain adaptation has been making great progress owing to the advancements in deep learning. Deep neural networks have demonstrated unrivaled success across multiple computer vision applications, including transfer learning and domain adaptation. This article outlines the latest research in domain adaptation using deep neural networks. It begins with an introduction to the concept of knowledge transfer in machine learning and the different paradigms of transfer learning. It provides a brief survey of nondeep-learning techniques and organizes the rapidly growing research in domain adaptation based on deep learning. It also highlights some drawbacks with the current state of research in this area and offers directions for future research.

Original languageEnglish (US)
Article number8103149
Pages (from-to)117-129
Number of pages13
JournalIEEE Signal Processing Magazine
Volume34
Issue number6
DOIs
StatePublished - Nov 2017

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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