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Introduction to Transfer Learning

Overview of attention for book
Cover of 'Introduction to Transfer Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 From Machine Learning to Transfer Learning
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    Chapter 3 Overview of Transfer Learning Algorithms
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    Chapter 4 Instance Weighting Methods
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    Chapter 5 Statistical Feature Transformation Methods
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    Chapter 6 Geometrical Feature Transformation Methods
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    Chapter 7 Theory, Evaluation, and Model Selection
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    Chapter 8 Pre-Training and Fine-Tuning
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    Chapter 9 Deep Transfer Learning
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    Chapter 10 Adversarial Transfer Learning
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    Chapter 11 Generalization in Transfer Learning
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    Chapter 12 Safe and Robust Transfer Learning
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    Chapter 13 Transfer Learning in Complex Environments
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    Chapter 14 Low-Resource Learning
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    Chapter 15 Transfer Learning for Computer Vision
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    Chapter 16 Transfer Learning for Natural Language Processing
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    Chapter 17 Transfer Learning for Speech Recognition
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    Chapter 18 Transfer Learning for Activity Recognition
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    Chapter 19 Federated Learning for Personalized Healthcare
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    Chapter 20 Concluding Remarks
Attention for Chapter 8: Pre-Training and Fine-Tuning
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Citations

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Chapter title
Pre-Training and Fine-Tuning
Chapter number 8
Book title
Introduction to Transfer Learning
Published by
Springer, Singapore, January 2023
DOI 10.1007/978-981-19-7584-4_8
Book ISBNs
978-9-81-197583-7, 978-9-81-197584-4
Authors

Wang, Jindong, Chen, Yiqiang

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 25%
Researcher 1 6%
Unknown 11 69%
Readers by discipline Count As %
Engineering 2 13%
Computer Science 1 6%
Mathematics 1 6%
Unknown 12 75%

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