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Meta-Learning: Theory, Algorithms and Applications

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Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI.

Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm.

The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources.

Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

Author(s): Lan Zou (editor)
Publisher: Academic Press
Year: 2022

Language: English
Pages: 402
City: London

Front Cover
Meta-Learning: Theory, Algorithms and Applications
Copyright
Dedication
Contents
Preface
Acknowledgments
Chapter 1: Meta-learning basics and background
1.1. Introduction
1.2. Meta-learning
1.2.1. Definitions
1.2.2. Evaluation
1.2.3. Datasets and benchmarks
1.3. Machine learning
1.3.1. Models
1.3.2. Limitations
1.3.3. Related concepts
1.3.4. Further Reading
1.4. Deep learning
1.4.1. Models
1.4.2. Limitations
1.4.3. Further readings
1.5. Transfer learning
1.5.1. Multitask learning
1.6. Few-shot learning
1.7. Probabilistic modeling
1.8. Bayesian inference
References
Part I: Theory & mechanisms
Chapter 2: Model-based meta-learning approaches
2.1. Introduction
2.2. Memory-augmented neural networks
2.2.1. Background knowledge
2.2.2. Methodology
Task setup
Memory retrieval
Least recently used access
2.2.3. Extended algorithm 1
2.2.4. Extended algorithm 2
2.3. Meta-networks
2.3.1. Background knowledge
2.3.2. Methodology
Slow weights and fast weights
Layer augmentation
2.3.3. Main loss functions and representation loss functions
2.4. Summary
References
Chapter 3: Metric-based meta-learning approaches
3.1. Introduction
3.2. Convolutional Siamese neural networks
3.2.1. Background knowledge
3.2.2. Methodology
Combination of the twin Siamese networks
Objective function
Optimization
3.2.3. Extended algorithm 1
3.3. Matching networks
3.3.1. Background knowledge
3.3.2. Methodology
The attention kernel
Full context embedding
Episode-based training
3.3.3. Extended algorithm 1
3.4. Prototypical networks
3.4.1. Background knowledge
3.4.2. Methodology
Bregman divergence requirement
3.4.3. Extended algorithm 1
3.4.4. Extended algorithm 2
3.4.5. Extended algorithm 3
3.5. Relation network
3.5.1. Background knowledge
3.5.2. Methodology
C-Way one-shot
C-Way K-shot
C-Way zero-shot
Objective function
3.6. Summary
References
Chapter 4: Optimization-based meta-learning approaches
4.1. Introduction
4.2. LSTM meta-learner
4.2.1. Background knowledge
Covariate shift
Batch normalization
Long short-term memory
Gradient-based optimization
4.2.2. Methodology
Gradient independent assumption and initialization
Meta-training and meta-testing batch normalization
Parameter sharing
4.3. Model-agnostic meta-learning
4.3.1. Background knowledge
Transfer learning
Fine-tuning
4.3.2. Methodology
Task adaptation
4.3.3. Illustration 1: Few-shot regression and few-shot classification
4.3.4. Illustration 2: Policy gradient reinforcement learning
4.3.5. Illustration 3: Meta-imitation learning
4.3.6. Related Algorithm 1: Meta-SGD
4.3.7. Related Algorithm 2: Feature reuse-The effectiveness of MAML
4.3.8. Related Algorithm 3: Adaptive hyperparameter generation for fast adaptation
4.4. Reptile
4.4.1. Background knowledge
First-order model-agnostic meta-learning
4.4.2. Methodology
4.4.2.1. Serial version
4.4.2.2. Parallel or batch version
The optimization assumption
Analysis
4.4.3. Related Algorithm 1
4.4.4. Related Algorithm 2
4.4.5. Related Algorithm 3
4.4.6. Related Algorithm 4
4.5. Summary
References
Part II: Applications
Chapter 5: Meta-learning for computer vision
5.1. Introduction
5.1.1. Limitations
5.2. Image classification
5.2.1. Introduction
Development
Approaches
Benchmarks
One-stage semisupervised learning
One-stage unsupervised learning
Multistage semisupervised learning
5.2.2. Decision boundary sharpness and few-shot image classification
5.2.3. Semisupervised few-shot image classification with refined prototypical network
5.2.4. Few-shot unsupervised image classification
5.2.5. One-shot image deformation
5.2.6. Heterogeneous multitask learning in image classification
5.2.7. Few-shot classification with transductive inference
5.2.8. Closed-form base learners
5.2.9. Long-tailed image classification
5.2.10. Image classification via incremental learning without forgetting
Comparison and contrast of iTAML and reptile
Lower bound of sample
5.2.11. Few-shot open set recognition
5.2.12. Deficiency of pretrained knowledge in few-shot learning
5.2.13. Bayesian strategy with deep kernel for regression and cross-domain image classification in a few-shot setting
5.2.14. Statistical diversity in personalized models of federated learning
5.2.15. Meta-learning deficiency in few-shot learning
5.3. Face recognition and face presentation attack
5.3.1. Introduction
Facial recognition
Face antispoofing
5.3.2. Person-specific talking head generation for unseen people and portrait painting in few-shot regimes
5.3.3. Face presentation attack and domain generalization
5.3.4. Anti-face-spoofing in few-shot and zero-shot scenarios
5.3.5. Generalized face recognition in the unseen domain
5.4. Object detection
5.4.1. Introduction
Approaches
Benchmarks
5.4.2. Long-tailed data object detection in few-shot scenarios
5.4.3. Object detection in few-shot scenarios
5.4.4. Unseen object detection and viewpoint estimation in low-data settings
5.5. Fine-grained image recognition
5.5.1. Introduction
Approaches
Benchmarks
5.5.2. Fine-grained visual categorization
5.5.3. One-shot fine-grained visual recognition
5.5.4. Few-shot fine-grained image recognition
5.6. Image segmentation
5.6.1. Introduction
Modern development
5.6.2. Multiobject few-shot semantic segmentation
5.6.3. Few-shot static object instance-level detection
5.7. Object tracking
5.7.1. Introduction
5.7.2. Offline object tracking
5.7.3. Real-time online object tracking
5.7.4. Real-time object tracking with channel pruning
One-shot channel pruning
5.7.5. Object tracking via instance detection
5.8. Label noise
5.8.1. Introduction
Approaches
Benchmarks
5.8.2. Reweighting examples through online approximation
5.8.3. Hallucinated clean representation for noisy-labeled visual recognition
5.8.4. Data valuation using reinforcement learning
5.8.5. Teacher-student networks for image classification on noisy labels
5.8.6. Sample reweighting function construction
5.8.7. Loss correction approach
5.8.8. Meta-relabeling through data coefficients
5.8.9. Meta-label correction
5.9. Superresolution
5.9.1. Introduction
Approaches
Datasets and benchmarks
5.9.2. Meta-transfer learning for zero-shot superresolution
5.9.3. LR-HR image pair superresolution
5.9.4. No-reference image quality assessment
5.10. Multimodal learning
5.10.1. Introduction
Deep learning approaches
Benchmarks
5.10.2. Visual question answering system
5.11. Other emerging topics
5.11.1. Domain generalization
5.11.2. High-accuracy 3D appearance-based gaze estimation in few-shot regimes
5.11.3. Benchmark of cross-domain few-shot learning in vision tasks
5.11.4. Latent embedding optimization in low-dimensional space
5.11.5. Image captioning
5.11.6. Memorization issue
5.11.7. Meta-pseudo label
5.12. Summary
References
Chapter 6: Meta-learning for natural language processing
6.1. Introduction
6.1.1. Limitations
6.2. Semantic parsing
6.2.1. Introduction
Development
Benchmarks
6.2.2. Natural language to structured query generation in few-shot learning
Implementation
6.2.3. Semantic parsing in low-resource scenarios
6.2.4. Context-dependent semantic parser with few-shot learning
6.3. Machine translation
6.3.1. Introduction
6.3.2. Multidomain neural machine translation in low-resource scenarios
6.3.3. Multilingual neural machine translation in few-shot scenarios
6.4. Dialogue system
6.4.1. Introduction
6.4.2. Few-shot personalizing dialogue generation
6.4.3. Domain adaptation in a dialogue system
6.4.4. Natural language generation by few-shot learning concerning task-oriented dialogue systems
6.5. Knowledge graph
6.5.1. Introduction
6.5.2. Multihop knowledge graph reasoning in few-shot scenarios
6.5.3. Knowledge graphs link prediction in few-shot scenarios
6.5.4. Knowledge base complex question answering
6.5.5. Named-entity recognition in cross-lingual scenarios
6.6. Relation extraction
6.6.1. Introduction
6.6.2. Few-shot supervised relation classification
6.6.3. Relation extraction with few-shot and zero-shot learning
6.7. Sentiment analysis
6.7.1. Introduction
Benchmark and dataset
6.7.2. Text emotion distribution learning with small samples
6.8. Emerging topics
6.8.1. Domain-specific word embedding under lifelong learning setting
Background knowledge
Methodology
6.8.2. Multilabel classification
Background knowledge
Methodology
6.8.3. Representation under a low-resource setting
Background knowledge
Methodology
6.8.4. Compositional generalization
Background knowledge
Methodology
6.8.5. Zero-shot transfer learning for query suggestion
Background knowledge
Methodology
6.9. Summary
References
Chapter 7: Meta-reinforcement learning
7.1. Background knowledge
7.1.1. Basic components of a deep reinforcement learning system
7.1.2. Model-based and model-free approaches
7.1.3. Simulated environments
7.1.4. Limitations of deep reinforcement learning
7.2. Meta-reinforcement learning introduction
7.2.1. Early development
7.2.2. Formalism
7.2.3. Fundamental components
7.3. Memory
7.3.1. External read-write memory for agents with multiple modalities
7.4. Meta-reinforcement learning methods
7.4.1. Continuous adaptation in nonstationary environments
Related Meta-RL algorithms for sample efficiency
7.4.2. Exploration with structured noise
Related Meta-RL approaches for exploration
7.4.3. Credit assignment
7.4.4. Second-order computation in MAML
Related Meta-RL algorithms based on MAML modifications
7.5. Reward signals and environments
7.5.1. Sparse extrinsic reward in procedurally generated environments
Related Meta-RL algorithms for reward signal
7.6. Benchmark
7.6.1. Meta-World
7.7. Visual navigation
7.7.1. Introduction
7.7.2. Visual navigation to unseen scenes
7.7.3. Transferable meta-knowledge in unsupervised visual navigation
7.8. Summary
References
Chapter 8: Meta-learning for healthcare
8.1. Introduction
Part I: Medical imaging computing
8.2. Image classification
8.2.1. Breast magnetic resonance imaging
8.2.2. Tongue identification
8.3. Lesion classification
8.3.1. Fine-grained skin disease classification
8.3.2. Difficulty-aware rare disease classification
8.3.3. Rare disease diagnostics: Skin lesion
8.4. Image segmentation
8.4.1. Medical ultra-resolution image segmentation
8.5. Image reconstruction
8.5.1. Chest and abdomen computed tomography image reconstruction
Part II: Electronic health records analysis
8.6. Electronic health records
8.6.1. Disease prediction in a low-resource setting
8.6.2. Disease classification in a few-shot setting
Part III: Application areas
8.7. Cardiology
8.7.1. Remote heart rate measurement in a few-shot setting
8.7.2. Customized pulmonary valve conduit reconstruction
8.7.3. Cardiac arrhythmia auto-screening
8.8. Disease diagnostics
8.8.1. Fine-grained disease classification under task heterogeneity
8.8.2. Clinical prognosis with Bayesian optimization
8.9. Data modality
8.9.1. Modality detection of biomedical images
8.10. Future work
References
Chapter 9: Meta-learning for emerging applications: Finance, building materials, graph neural networks, program synthesis ...
9.1. Introduction
9.2. Finance and economics
9.2.1. Introduction
Approaches
9.2.2. Detection of credit card transaction fraud
9.2.3. Task-agnostic meta-learner with inequality measurement in economics
Economic inequality measure
9.3. Building materials
9.3.1. Defect (crack) recognition in concrete in reinforcement learning
9.4. Graph neural network
9.4.1. Introduction
9.4.2. Node classification on graphs with few-shot novel labels
9.4.3. Local subgraphs for node classification and link prediction
9.4.4. Adversarial attacks of node classification
Comparion and contrast of AQ and prototypical meta-learning
9.4.5. Dual-graph structured approach with instance- and distribution-level relations
9.5. Program synthesis
9.5.1. Syntax-guided synthesis
9.6. Transportation
9.6.1. Introduction
9.6.2. Traffic signal control
9.6.3. Continuous trajectory estimation for lane changes under a few-shot setting
9.6.4. Urban traffic prediction based on spatio-temporal correlation
9.7. Cold-start problems in recommendation systems
9.7.1. Introduction
9.7.2. Continuously adding new items
9.7.3. Context-aware cross-domain recommendation cold-start under a few-shot setting
9.7.4. User preference estimator
9.7.5. Memory-augmented recommendation system meta-optimization
9.7.6. Meta-learner with heterogeneous information networks
9.8. Climate science
9.8.1. Introduction
9.8.2. Critical incident detection
9.9. Summary
References
Index
Back Cover