Overview
- The first book focused on domain adaptation for visual applications
- Provides a comprehensive experimental study, highlighting the strengths and weaknesses of popular methods, and introducing new and more challenging datasets
- Presents an historical overview of research in this area
- Covers such tasks as object detection, image segmentation and video application, where the need for domain adaptation has been rarely addressed by the community
- Considers real-world, industrial applications, and solutions for cases where existing methods might not be applicable
- Includes supplementary material: sn.pub/extras
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
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Keywords
- Computer Vision
- Visual Applications
- Image Categorization
- Pattern Recognition
- Data Analytics
- Unsupervised Domain Adaptation
- Transductive Transfer Learning
- Domain Shift
- Feature Transformation
- Subspace Learning
- Landmark Selection
- Maximum Mean Discrepancy
- Grassman Manifold
- Geodesic Flow
- Subspace Alignment
- Marginalized Denoising Autoencoders
- Deep Learning
- Domain-Adversarial Training
Table of contents (16 chapters)
-
Shallow Domain Adaptation Methods
-
Deep Domain Adaptation Methods
-
Beyond Image Classification
-
Beyond Domain Adaptation: Unifying Perspectives
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Domain Adaptation in Computer Vision Applications
Editors: Gabriela Csurka
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-319-58347-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-58346-4Published: 02 October 2017
Softcover ISBN: 978-3-319-86383-2Published: 17 May 2018
eBook ISBN: 978-3-319-58347-1Published: 10 September 2017
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: X, 344
Number of Illustrations: 6 b/w illustrations, 101 illustrations in colour
Topics: Image Processing and Computer Vision, Artificial Intelligence, Computer Appl. in Administrative Data Processing