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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
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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
Attention for Chapter 9: Simultaneous Deep Transfer Across Domains and Tasks
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Citations

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Chapter title
Simultaneous Deep Transfer Across Domains and Tasks
Chapter number 9
Book title
Domain Adaptation in Computer Vision Applications
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-58347-1_9
Book ISBNs
978-3-31-958346-4, 978-3-31-958347-1
Authors

Judy Hoffman, Eric Tzeng, Trevor Darrell, Kate Saenko, Hoffman, Judy, Tzeng, Eric, Darrell, Trevor, Saenko, Kate

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 635 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Germany 2 <1%
Japan 1 <1%
Singapore 1 <1%
Unknown 629 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 175 28%
Student > Master 109 17%
Researcher 75 12%
Student > Bachelor 35 6%
Student > Doctoral Student 28 4%
Other 59 9%
Unknown 154 24%
Readers by discipline Count As %
Computer Science 334 53%
Engineering 86 14%
Agricultural and Biological Sciences 10 2%
Mathematics 7 1%
Physics and Astronomy 5 <1%
Other 23 4%
Unknown 170 27%

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