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Domain Adaptation in Computer Vision Applications

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Cover of 'Domain Adaptation in Computer Vision Applications'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 10: Domain-Adversarial Training of Neural Networks
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Chapter title
Domain-Adversarial Training of Neural Networks
Chapter number 10
Book title
Domain Adaptation in Computer Vision Applications
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-58347-1_10
Book ISBNs
978-3-31-958346-4, 978-3-31-958347-1
Authors

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

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2,871 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 <1%
Canada 3 <1%
France 3 <1%
United Kingdom 3 <1%
Italy 2 <1%
Switzerland 1 <1%
Australia 1 <1%
Turkey 1 <1%
Portugal 1 <1%
Other 7 <1%
Unknown 2842 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 679 24%
Student > Master 513 18%
Researcher 315 11%
Student > Bachelor 213 7%
Student > Doctoral Student 94 3%
Other 287 10%
Unknown 770 27%
Readers by discipline Count As %
Computer Science 1310 46%
Engineering 386 13%
Mathematics 55 2%
Physics and Astronomy 54 2%
Agricultural and Biological Sciences 36 1%
Other 181 6%
Unknown 849 30%