Inpainting and Denoising Challenges
The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generat
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Sergio Escalera Stephane Ayache Jun Wan Meysam Madadi Umut Güçlü Xavier Baró Editors
Inpainting and Denoising Challenges
The Springer Series on Challenges in Machine Learning Series editors Hugo Jair Escalante, Astrofisica Optica y Electronica, INAOE, Puebla, Mexico Isabelle Guyon, ChaLearn, Berkeley, CA, USA Sergio Escalera , Universitat de Barcelona, Computer Vision Center, Barcelona, Spain
The books in this innovative series collect papers written in the context of successful competitions in machine learning. They also include analyses of the challenges, tutorial material, dataset descriptions, and pointers to data and software. Together with the websites of the challenge competitions, they offer a complete teaching toolkit and a valuable resource for engineers and scientists.
More information about this series at http://www.springer.com/series/15602
Sergio Escalera • Stephane Ayache • Jun Wan Meysam Madadi • Umut G¨uçl¨u • Xavier Baró Editors
Inpainting and Denoising Challenges
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Editors Sergio Escalera Department of Mathematics & Informatics Universitat de Barcelona Computer Vision Center Barcelona, Spain Jun Wan Institute of Automation Chinese Academy of Sciences Beijing, China Umut G¨uçl¨u Radboud University Nijmegen Nijmegen, The Netherlands
Stephane Ayache Aix-Marseille University Marseille, France Meysam Madadi Computer Vision Center Autonomous University of Barcelona Bellaterra, Barcelona, Spain Xavier Baró Open University of Catalonia Barcelona, Spain
ISSN 2520-131X ISSN 2520-1328 (electronic) The Springer Series on Challenges in Machine Learning ISBN 978-3-030-25613-5 ISBN 978-3-030-25614-2 (eBook) https://doi.org/10.1007/978-3-030-25614-2 © Springer Nature Switzerland AG 2019 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, express 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
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