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Normalization Techniques in Deep Learning
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Normalization Techniques in Deep Learning 深度学习中的规范化技术
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Normalization
Techniques in
Deep Learning
Synthesis Lectures on Computer Vision
Lei Huang

Synthesis Lectures on Computer Vision
Series Editors
Gerard Medioni, University of Southern California, Los Angeles, CA, USA
Sven Dickinson, Department of Computer Science, University of Toronto, Toronto, ON,
Canada

This series publishes on topics pertaining to computer vision and pattern recognition.
The scope follows the purview of premier computer science conferences, and includes
the science of scene reconstruction, event detection, video tracking, object recognition,
3D pose estimation, learning, indexing, motion estimation, and image restoration. As a
scientific discipline, computer vision is concerned with the theory behind artificial systems
that extract information from images. The image data can take many forms, such as
video sequences, views from multiple cameras, or multi-dimensional data from a medical
scanner. As a technological discipline, computer vision seeks to apply its theories and
models for the construction of computer vision systems, such as those in self-driving
cars/navigation systems, medical image analysis, and industrial robots.

Lei Huang
Normalization Techniques
in Deep Learning

Lei Huang
Beihang University
Beijing, China
ISSN 2153-1056 ISSN 2153-1064 (electronic)
Synthesis Lectures on Computer Vision
ISBN 978-3-031-14594-0 ISBN 978-3-031-14595-7 (eBook)
https://doi.org/10.1007/978-3-031-14595-7
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
2022
This work is subject to copyright. All rights are solely and exclusively licensed 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 h ave 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
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