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Domain Adaptation in Computer Vision Applications

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Table of Contents

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    Book Overview
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    Chapter 1 A Comprehensive Survey on Domain Adaptation for Visual Applications
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    Chapter 2 A Deeper Look at Dataset Bias
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    Chapter 3 Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation
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    Chapter 4 Unsupervised Domain Adaptation Based on Subspace Alignment
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    Chapter 5 Learning Domain Invariant Embeddings by Matching Distributions
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    Chapter 6 Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation
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    Chapter 7 What to Do When the Access to the Source Data Is Constrained?
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    Chapter 8 Correlation Alignment for Unsupervised Domain Adaptation
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    Chapter 9 Simultaneous Deep Transfer Across Domains and Tasks
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    Chapter 10 Domain-Adversarial Training of Neural Networks
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    Chapter 11 Unsupervised Fisher Vector Adaptation for Re-identification
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    Chapter 12 Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA
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    Chapter 13 From Virtual to Real World Visual Perception Using Domain Adaptation—The DPM as Example
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    Chapter 14 Generalizing Semantic Part Detectors Across Domains
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    Chapter 15 A Multisource Domain Generalization Approach to Visual Attribute Detection
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    Chapter 16 Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives
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Citations

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Title
Domain Adaptation in Computer Vision Applications
Published by
Springer International Publishing, January 2017
DOI 10.1007/978-3-319-58347-1
ISBNs
978-3-31-958347-1, 978-3-31-958346-4
Authors

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky

Editors

Gabriela Csurka

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 26%
Student > Master 7 15%
Student > Bachelor 5 11%
Student > Postgraduate 3 7%
Student > Doctoral Student 3 7%
Other 6 13%
Unknown 10 22%
Readers by discipline Count As %
Computer Science 24 52%
Engineering 9 20%
Physics and Astronomy 1 2%
Mathematics 1 2%
Unknown 11 24%