Meta-Learning and Few-Shot Adaptation in Foundation Models
Chapter 1: Foundations of Meta-Learning Revisited
The Meta-Learning Problem Formulation
Taxonomy of Meta-Learning Approaches
Challenges in Applying Meta-Learning to Foundation Models
Evaluation Protocols for Few-Shot Learning
Chapter 2: Advanced Gradient-Based Meta-Learning
Model-Agnostic Meta-Learning (MAML)
First-Order MAML (FOMAML) and Reptile
Implicit MAML (iMAML)
Addressing Stability and Gradient Variance
Scalability Considerations for Foundation Models
Hands-on Practical: Implementing FOMAML for Model Adaptation
Chapter 3: Advanced Metric-Based Meta-Learning
Prototypical Networks Revisited
Relation Networks for Few-Shot Learning
Matching Networks with Attention
Deep Metric Learning Techniques
Adapting Metric Learning for High-Dimensional Embeddings
Practice: Implementing Prototypical Networks with Foundation Model Embeddings
Chapter 4: Optimization Perspectives on Meta-Learning
Meta-Learning as Bilevel Optimization
Algorithms for Solving Bilevel Problems
Connections to Hyperparameter Optimization
Meta-Learning Initialization Strategies
Theoretical Convergence Analysis
Chapter 5: Few-Shot Adaptation Strategies for Foundation Models
Parameter-Efficient Fine-Tuning (PEFT) Overview
Adapter Modules for Foundation Models
Low-Rank Adaptation (LoRA)
Prompt Tuning and Prefix Tuning
Comparing PEFT and Meta-Learning Approaches
Hybrid Adaptation Strategies
Hands-on Practical: Adapting a Foundation Model using LoRA
Chapter 6: Scaling Meta-Learning Implementations
Computational Challenges of Meta-Gradients
Memory Optimization Techniques
Distributed Meta-Learning Strategies
Efficient Task Sampling and Batching
Approximation Methods for Scalability
Benchmarking Scalable Implementations
Chapter 7: Advanced Topics and Theoretical Considerations
Bayesian Meta-Learning Approaches
Continual Meta-Learning
Meta-Learning for Reinforcement Learning
Generalization Bounds in Meta-Learning
Information Theoretic Perspectives
Open Problems and Research Directions
Adapting Metric Learning for High-Dimensional Embeddings
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- Prototypical Networks for Few-Shot Learning, Jake Snell, Kevin Swersky, Richard Zemel, 2017 Advances in Neural Information Processing Systems, Vol. 30 (NeurIPS) - Introduces Prototypical Networks, a seminal metric-based meta-learning algorithm frequently used with high-dimensional embeddings from foundation models.
- Learning to Compare: Relation Network for Few-Shot Learning, Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales, 2018 2018 IEEE Conference on Computer Vision and Pattern Recognition - Introduces Relation Networks, a meta-learning approach that learns a non-linear function to compute a similarity score between embeddings, relevant for refining distance metrics.
- The Johnson-Lindenstrauss Lemma and its applications to data streams, Piotr Indyk, Rajeev Motwani, 1998 Proceedings of the thirtieth annual ACM symposium on Theory of computing (Association for Computing Machinery (ACM)) DOI: 10.1145/276698.276856 - Presents applications of the Johnson-Lindenstrauss lemma, providing theoretical underpinning for using random projections for dimensionality reduction while preserving pairwise distances.
- Revisiting Metric Learning for Few-Shot Image Classification, Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, 2020 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (IEEE) DOI: 10.1109/CVPRW50498.2020.00049 - Addresses practical issues in metric learning for few-shot classification, including the impact of high-dimensional feature spaces and strategies like L2 normalization and refined metric learning.