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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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