Introduction to Transfer Learning: Algorithms and Practice

Preface
Acknowledgments
Contents
Acronyms
Symbols
Part I Foundations
1 Introduction
	1.1 Transfer Learning
	1.2 Related Research Fields
	1.3 Why Transfer Learning?
		1.3.1 Big Data vs. Less Annotation
		1.3.2 Big Data vs. Poor Computation
		1.3.3 Limited Data vs. Generalization Requirements
		1.3.4 Pervasive Model vs. Personal Need
		1.3.5 For Specific Applications
	1.4 Taxonomy of Transfer Learning
		1.4.1 Taxonomy by Feature Space
		1.4.2 Taxonomy by Target Domain Labels
		1.4.3 Taxonomy by Learning Methodology
		1.4.4 Taxonomy by Online or Offline Learning
	1.5 Transfer Learning in Academia and Industry
	1.6 Overview of Transfer Learning Applications
		1.6.1 Computer Vision
		1.6.2 Natural Language Processing
		1.6.3 Speech
		1.6.4 Ubiquitous Computing and Human–Computer Interaction
		1.6.5 Healthcare
		1.6.6 Other Applications
	References
2 From Machine Learning to Transfer Learning
	2.1 Machine Learning Basics
		2.1.1 Machine Learning
		2.1.2 Structural Risk Minimization
		2.1.3 Probability Distribution
	2.2 Definition of Transfer Learning
		2.2.1 Domains
		2.2.2 Formal Definition
	2.3 Fundamental Problems in Transfer Learning
		2.3.1 When to Transfer
		2.3.2 Where to Transfer
		2.3.3 How to Transfer
	2.4 Negative Transfer Learning
	2.5 A Complete Transfer Learning Process
	References
3 Overview of Transfer Learning Algorithms
	3.1 Measuring Distribution Divergence
	3.2 Unified Representation for Distribution Divergence
		3.2.1 Estimation of Balance Factor μ
	3.3 A Unified Framework for Transfer Learning
		3.3.1 Instance Weighting Methods
		3.3.2 Feature Transformation Methods
		3.3.3 Model Pre-training
		3.3.4 Summary
	3.4 Practice
		3.4.1 Data Preparation
		3.4.2 Baseline Model: K-Nearest Neighbors
	References
4 Instance Weighting Methods
	4.1 Problem Definition
	4.2 Instance Selection Methods
		4.2.1 Non-reinforcement Learning-Based Methods
		4.2.2 Reinforcement Learning-Based Methods
	4.3 Weight Adaptation Methods
	4.4 Practice
	4.5 Summary
	References
5 Statistical Feature Transformation Methods
	5.1 Problem Definition
	5.2 Maximum Mean Discrepancy-Based Methods
		5.2.1 The Basics of MMD
		5.2.2 MMD-Based Transfer Learning
		5.2.3 Computation and Optimization
		5.2.4 Extensions of MMD-Based Transfer Learning
	5.3 Metric Learning-Based Methods
		5.3.1 Metric Learning
		5.3.2 Metric Learning for Transfer Learning
	5.4 Practice
	5.5 Summary
	References
6 Geometrical Feature Transformation Methods
	6.1 Subspace Learning Methods
		6.1.1 Subspace Alignment
		6.1.2 Correlation Alignment
	6.2 Manifold Learning Methods
		6.2.1 Manifold Learning
		6.2.2 Manifold Learning for Transfer Learning
	6.3 Optimal Transport Methods
		6.3.1 Optimal Transport
		6.3.2 Optimal Transport for Transfer Learning
	6.4 Practice
	6.5 Summary
	References
7 Theory, Evaluation, and Model Selection
	7.1 Transfer Learning Theory
		7.1.1 Theory Based on H-Divergence
		7.1.2 Theory Based on H H-Distance
		7.1.3 Theory Based on Discrepancy Distance
		7.1.4 Theory Based on Labeling Function Difference
	7.2 Metric and Evaluation
	7.3 Model Selection
		7.3.1 Importance Weighted Cross Validation
		7.3.2 Transfer Cross Validation
	7.4 Summary
	References
Part II Modern Transfer Learning
8 Pre-Training and Fine-Tuning
	8.1 How Transferable Are Deep Networks
	8.2 Pre-Training and Fine-Tuning
		8.2.1 Benefits of Pre-Training and Fine-Tuning
	8.3 Regularization for Fine-Tuning
	8.4 Pre-Trained Models for Feature Extraction
	8.5 Learn to Pre-Training and Fine-Tuning
	8.6 Practice
	8.7 Summary
	References
9 Deep Transfer Learning
	9.1 Overview
	9.2 Network Architectures for Deep Transfer Learning
		9.2.1 Single-Stream Architecture
		9.2.2 Two-Stream Architecture
	9.3 Distribution Adaptation in Deep Transfer Learning
	9.4 Structure Adaptation for Deep Transfer Learning
		9.4.1 Batch Normalization
		9.4.2 Multi-view Structure
		9.4.3 Disentanglement
	9.5 Knowledge Distillation
	9.6 Practice
		9.6.1 Network Structure
		9.6.2 Loss
		9.6.3 Train and Test
	9.7 Summary
	References
10 Adversarial Transfer Learning
	10.1 Generative Adversarial Networks
	10.2 Distribution Adaptation for Adversarial Transfer Learning
	10.3 Maximum Classifier Discrepancy for Adversarial Transfer Learning
	10.4 Data Generation for Adversarial Transfer Learning
	10.5 Practice
		10.5.1 Domain Discriminator
		10.5.2 Measuring Distribution Divergence
		10.5.3 Gradient Reversal Layer
	10.6 Summary
	References
11 Generalization in Transfer Learning
	11.1 Domain Generalization
	11.2 Data Manipulation
		11.2.1 Data Augmentation and Generation
		11.2.2 Mixup-Based Domain Generalization
	11.3 Domain-Invariant Representation Learning
		11.3.1 Domain-Invariant Component Analysis
		11.3.2 Deep Domain Generalization
		11.3.3 Disentanglement
	11.4 Other Learning Paradigms for Domain Generalization
		11.4.1 Ensemble Learning
		11.4.2 Meta-Learning for Domain Generalization
		11.4.3 Other Learning Paradigms
	11.5 Domain Generalization Theory
		11.5.1 Average Risk Estimation Error Bound
		11.5.2 Generalization Risk Bound
	11.6 Practice
		11.6.1 Dataloader in Domain Generalization
		11.6.2 Training and Testing
		11.6.3 Examples: ERM and CORAL
	11.7 Summary
	References
12 Safe and Robust Transfer Learning
	12.1 Safe Transfer Learning
		12.1.1 Can Transfer Learning Models Be Attacked?
		12.1.2 Reducing Defect Inheritance
		12.1.3 ReMoS: Relevant Model Slicing
	12.2 Federated Transfer Learning
		12.2.1 Federated Learning
		12.2.2 Personalized Federated Learning for Non-I.I.D. Data
			12.2.2.1 Model Adaptation for Personalized Federated Learning
			12.2.2.2 Similarity-Guided Personalized Federated Learning
	12.3 Data-Free Transfer Learning
		12.3.1 Information Maximization Methods
		12.3.2 Feature Matching Methods
	12.4 Causal Transfer Learning
		12.4.1 What is Causal Relation?
		12.4.2 Causal Relation for Transfer Learning
	12.5 Summary
	References
13 Transfer Learning in Complex Environments
	13.1 Imbalanced Transfer Learning
	13.2 Multi-Source Transfer Learning
	13.3 Open Set Transfer Learning
	13.4 Time Series Transfer Learning
		13.4.1 AdaRNN for Time Series Forecasting
		13.4.2 DIVERSIFY for Time Series Classification
	13.5 Online Transfer Learning
	13.6 Summary
	References
14 Low-Resource Learning
	14.1 Compressing Transfer Learning Models
	14.2 Semi-supervised Learning
		14.2.1 Consistency Regularization Methods
		14.2.2 Pseudo Labeling and Thresholding Methods
	14.3 Meta-learning
		14.3.1 Model-Based Meta-learning
		14.3.2 Metric-Based Meta-learning
		14.3.3 Optimization-Based Meta-learning
	14.4 Self-supervised Learning
		14.4.1 Constructing Pretext Tasks
		14.4.2 Contrastive Self-supervised Learning
	14.5 Summary
	References
Part III Applications of Transfer Learning
15 Transfer Learning for Computer Vision
	15.1 Objection Detection
		15.1.1 Task and Dataset
		15.1.2 Load Data
		15.1.3 Model
		15.1.4 Train and Test
	15.2 Neural Style Transfer
		15.2.1 Load Data
		15.2.2 Model
		15.2.3 Train
	References
16 Transfer Learning for Natural Language Processing
	16.1 Emotion Classification
	16.2 Model
	16.3 Train and Test
	16.4 Pre-training and Fine-tuning
	References
17 Transfer Learning for Speech Recognition
	17.1 Cross-Domain Speech Recognition
		17.1.1 MMD and CORAL for ASR
		17.1.2 CMatch Algorithm
		17.1.3 Experiments and Results
	17.2 Cross-Lingual Speech Recognition
		17.2.1 Adapter
		17.2.2 Cross-Lingual Adaptation with Adapters
		17.2.3 Advanced Algorithm: MetaAdapter and SimAdapter
		17.2.4 Results and Discussion
	References
18 Transfer Learning for Activity Recognition
	18.1 Task and Dataset
	18.2 Feature Extraction
	18.3 Source Selection
	18.4 Activity Recognition Using TCA
	18.5 Activity Recognition Using Deep Transfer Learning
	References
19 Federated Learning for Personalized Healthcare
	19.1 Task and Dataset
		19.1.1 Dataset
		19.1.2 Data Splits
		19.1.3 Model Architecture
	19.2 FedAvg: Baseline Algorithm
		19.2.1 Clients Update
		19.2.2 Communication on the Server
		19.2.3 Results
	19.3 AdaFed: Adaptive Batchnorm for Federated Learning
		19.3.1 Similarity Matrix Computation
		19.3.2 Communication on the Server
		19.3.3 Results
	References
20 Concluding Remarks
	References
A Useful Distance Metrics
	A.1 Euclidean Distance
	A.2 Minkowski Distance
	A.3 Mahalanobis Distance
	A.4 Cosine Similarity
	A.5 Mutual Information
	A.6 Pearson Correlation
	A.7 Jaccard Index
	A.8 KL and JS Divergence
	A.9 Maximum Mean Discrepancy
	A.10 A-distance
	A.11 Hilbert–Schmidt Independence Criterion
B Popular Datasets in Transfer Learning
	B.1 Digit Recognition Datasets
	B.2 Object Recognition and Image Classification Datasets
	B.3 Text Classification Datasets
	B.4 Activity Recognition Datasets
C Venues Related to Transfer Learning
	C.1 Machine Learning and AI
	C.2 Computer Vision and Multimedia
	C.3 Natural Language Processing and Speech
	C.4 Ubiquitous Computing and Human–Computer Interaction
	C.5 Data Mining
	Reference