Normalization Techniques in Deep Learning

Lei Huang (Autor)

Buch | Softcover

XI, 110 Seiten

2023 | 1st ed. 2022
Springer International Publishing (Verlag)
978-3-031-14597-1 (ISBN)

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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.

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.

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.

Erscheinungsdatum	11.10.2023
Reihe/Serie	Synthesis Lectures on Computer Vision
Zusatzinfo	XI, 110 p. 26 illus., 21 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	168 x 240 mm
Gewicht	222 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
Schlagworte	Artificial Intelligence • Batch Normalization • computer vision • Deep Neural Networks (DNNs) • domain adaptation • generative adversarial networks • Image Classifcation • Image Style Transfer • machine learning • Natural Language Processing (NLP) • Normalization Techniques • Optimization • Statistical Learning • Weight Normalization
ISBN-10	3-031-14597-6 / 3031145976
ISBN-13	978-3-031-14597-1 / 9783031145971
Zustand	Neuware