Normalization Techniques in Deep Learning
1st Edition
Published on 8. October 2022
XI, 110 pages
978-3-031-14595-7 (ISBN)
Description
This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 110 p. 26 illus., 21 illus. in color.
File size
2,91 MB
ISBN-13
978-3-031-14595-7 (9783031145957)
DOI
10.1007/978-3-031-14595-7
Schweitzer Classification
Other editions
Additional editions
Book
10/2022
Springer
€58.84
Shipment within 15-20 days
Person
Lei Huang, Ph.D., is an Associate Professor at Beihang University. His current research interests include normalization techniques involving methods, theories, and applications in training deep neural networks (DNNs). He also has wide interests in representation and optimization of deep learning theory and computer vision tasks. Dr. Huang serves as a reviewer for top-tier conferences and journals in machine learning and computer vision.
Content
Introduction.- Motivation and Overview of Normalization in DNNs.- A General View of Normalizing Activations.- A Framework for Normalizing Activations as Functions.- Multi-Mode and Combinational Normalization.- BN for More Robust Estimation.- Normalizing Weights.- Normalizing Gradients.- Analysis of Normalization.- Normalization in Task-specific Applications.- Summary and Discussion.
