Normalization techniques in deep learning Lei Huang

Format:

Book

Author/Creator:

Huang, Lei, author.

Series:

Synthesis lectures on computer vision 2153-1064

Language:

English

Subjects (All):

Deep learning (Machine learning).

Physical Description:

1 online resource

Edition:

Second edition

Place of Publication:

Cham Springer [2026]

Summary:

This book surveys normalization techniques with a deep analysis in training deep neural networks. 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. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs. This Second Edition builds upon the original material with the addition of more recent proposed methods and expanded technical details for new normalization methods and network architectures tailored to specific tasks. In addition, this book: Presents a research landscape for normalization techniques, including methods, analysis, and applications Features normalization methods that improve the training stability, optimization efficiency, and generalization of DNNs Provides valuable guidelines for selecting normalization techniques to use in training DNNs for various applications

Contents:

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

Notes:

Includes bibliographical references

Online resource; title from PDF title page (SpringerLink, viewed July 30, 2026)

ISBN:

9783032199911

3032199913

OCLC:

1609312165

Access Restriction:

Restricted for use by site license