Domain Adaptation in Computer Vision with Deep Learning
This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to
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omain Adaptation in Computer Vision with Deep Learning
Domain Adaptation in Computer Vision with Deep Learning
Hemanth Venkateswara • Sethuraman Panchanathan Editors
Domain Adaptation in Computer Vision with Deep Learning
Editors Hemanth Venkateswara Center for Cognitive Ubiquitous Computing (CUbiC) School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe, AZ, USA
Sethuraman Panchanathan Center for Cognitive Ubiquitous Computing (CUbiC) School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe, AZ, USA
ISBN 978-3-030-45528-6 ISBN 978-3-030-45529-3 (eBook) https://doi.org/10.1007/978-3-030-45529-3 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
The focus of this book on Domain Adaptation in Computer Vision With Deep Learning is to serve as a one-stop shop for deep learning-based computer vision research in domain adaptation. The book is also meant to be a concise guide for navigating the vast amount of research in this area. The book is organized into four parts that provide a summary of research in domain adaptation. It begins with an introduction to domain adaptation and a survey of non-deep learning-based research in the first part. In Parts II and III, the book discusses feature alignment and image alignment techniques for domain adaptation. Part IV of the book outlines novel approaches detailing the future of research in domain adaptation. A diverse set of experts were invited to contribute comprehensive and complementary perspectives. The editors thank the contributing authors for sharing their perspectives. The editors also acknowledge the funding support
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