finelybook
Domain Adaptation in Computer Vision Applications (Advances in Computer Vision and Pattern Recognition)
ISBN-10 书号: 3319583468
ISBN-13 书号: 9783319583464
Edition 版本: 1st ed. 2017
Publisher Finelybook 出版日期: 2017-10-11
Pages: 344
Book Description
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
Chapter 1 A Comprehensive Survey On Domain Adaptation For Visual Applications
Chapter 2 A Deeper Look At Dataset Bias
Part I Shallow Domain Adaptation Methods
Chapter 3 Geodesic Flow Kernel And Landmarks: Kernel Methods For Unsupervised Domain Adaptation
Chapter 4 Unsupervised Domain Adaptation Based On Subspace Alignment
Chapter 5 Learning Domain Invariant Embeddings By Matching Distributions
Chapter 6 Adaptive Transductive Transfer Machines: A Pipeline For Unsupervised Domain Adaptation
Chapter 7 What To Do When The Access To The Source Data Is Constrained?
Part II Deep Domain Adaptation Methods
Chapter 8 Correlation Alignment For Unsupervised Domain Adaptation
Chapter 9 Simultaneous Deep Transfer Across Domains And Tasks
Chapter 10 Domain-Adversarial Training Of Neural Networks
Part III Beyond Image Classification
Chapter 11 Unsupervised Fisher Vector Adaptation For Re-Identification
Chapter 12 Semantic Segmentation Of Urban Scenes Via Domain Adaptation Of Synthia
Chapter 13 From Virtual To Real World Visual Perception Using Domain Adaptation---The Dpm As Example
Chapter 14 Generalizing Semantic Part Detectors Across Domains
Part IV Beyond Domain Adaptation: Unifying Perspectives
Chapter 15 A Multisource Domain Generalization Approach To Visual Attribute Detection
Chapter 16 Unifying Multi-Domain Multitask Learning: Tensor And Neural Network Perspectives
