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Csurka G. (Ed.) Domain Adaptation in Computer Vision Applications

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Csurka G. (Ed.) Domain Adaptation in Computer Vision Applications
Springer International Publishing AG, 2017. — 338 p. — (Advances in Computer Vision and Pattern Recognition) — ISBN 978-3-319-58346-4.
This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes.
Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning.
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.
Contents
A Comprehensive Survey on Domain Adaptation for Visual Applications
A Deeper Look at Dataset Bias
Shallow Domain Adaptation Methods
Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation Based on Subspace Alignment
Learning Domain Invariant Embeddings by Matching Distributions
Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation
What to Do When the Access to the Source Data Is Constrained?
Deep Domain Adaptation Methods
Correlation Alignment for Unsupervised Domain Adaptation
Simultaneous Deep Transfer Across Domains and Tasks
Domain-Adversarial Training of Neural Networks
Beyond Image Classification
Unsupervised Fisher Vector Adaptation for Re-
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA
From Virtual to Real World Visual Perception Using Domain Adaptation—The DPM as Example.
Generalizing Semantic Part Detectors Across Domains
Beyond Domain Adaptation: Unifying Perspectives
A Multisource Domain Generalization Approach to Visual Attribute Detection
Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives
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