Introduction to Domain Adaptation

Hemanth Venkateswara () and Sethuraman Panchanathan ()
Additional contact information
Hemanth Venkateswara: Arizona State University, Center for Cognitive Ubiquitous Computing (CUbiC), School of Computing Informatics and Decision Systems Engineering
Sethuraman Panchanathan: Arizona State University, Center for Cognitive Ubiquitous Computing (CUbiC), School of Computing Informatics and Decision Systems Engineering

Chapter Chapter 1 in Domain Adaptation in Computer Vision with Deep Learning, 2020, pp 3-21 from Springer

Abstract: Abstract This chapter provides a formal introduction to transfer learning. We define transfer learning and provide examples of different forms of transfer learning in machine learning including domain adaptation. We outline different forms of domain adaptation and derive it’s performance bounds. The final section presents a brief description of the chapters in the book.

Keywords: Domain adaptation; Transfer learning; Knowledge transfer; Domain discrepancy; Domain alignment (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-45529-3_1

Ordering information: This item can be ordered from
http://www.springer.com/9783030455293

DOI: 10.1007/978-3-030-45529-3_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().