Table of contents
About this book
This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation.
Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation.
This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Editors and affiliations
- Book Title Domain Adaptation in Computer Vision with Deep Learning
- DOI https://doi.org/10.1007/978-3-030-45529-3
- Copyright Information Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Hardcover ISBN 978-3-030-45528-6
- Softcover ISBN 978-3-030-45531-6
- eBook ISBN 978-3-030-45529-3
- Edition Number 1
- Number of Pages XI, 256
- Number of Illustrations 21 b/w illustrations, 55 illustrations in colour
Computer Imaging, Vision, Pattern Recognition and Graphics
Signal, Image and Speech Processing
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