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
Deep Metric Learning Techniques
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- Dimensionality Reduction by Learning an Invariant Mapping, Raia Hadsell, Sumit Chopra, Yann LeCun, 2006 2006 IEEE Computer Society Conference (IEEE) DOI: 10.1109/CVPR.2006.100 - Introduces the contrastive loss function for learning a similarity metric, a work in deep metric learning.
- FaceNet: A Unified Embedding for Face Recognition and Clustering, Florian Schroff, Dmitry Kalenichenko, James Philbin, 2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2015.7298682 - Popularized the triplet loss and online triplet mining strategies for learning face embeddings.
- Prototypical Networks for Few-Shot Learning, Jake Snell, Kevin Swersky, Richard S. Zemel, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017), Vol. 30 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) - Presents a metric-based meta-learning algorithm (Prototypical Networks) that uses learned embeddings, directly applicable to the section's content.