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
Evaluation Protocols for Few-Shot Learning
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- Human-level concept learning through probabilistic program induction, Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum, 2015 Science, Vol. 350 (American Association for the Advancement of Science) DOI: 10.1126/science.aab3050 - This foundational paper introduced the Omniglot dataset and demonstrated few-shot learning capabilities, popularizing the concept of learning from very few examples and the N-way K-shot task formulation.
- Optimization as a Model for Few-Shot Learning, Sachin Ravi, Hugo Larochelle, 2017 International Conference on Learning Representations (ICLR) - This paper is widely recognized for introducing the miniImageNet benchmark and a meta-learning framework, establishing common evaluation practices for N-way K-shot tasks in the meta-learning community.
- Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples, Vincent Triantafillou, Tyler Zhu, Kevin Han, Dian Peng, Andrei Bursuc, Frank Wood, Sergey Levine, Chelsea Finn, 2019 International Conference on Learning Representations (ICLR) - Introduces Meta-Dataset, a challenging benchmark that combines multiple datasets, promoting evaluation robustness across heterogeneous few-shot learning scenarios and pushing beyond single-dataset evaluations.
- Generalizing from a Few Examples: A Survey on Few-Shot Learning, Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni, 2020 ACM Computing Surveys, Vol. 53 (ACM) DOI: 10.1145/3386252 - This comprehensive survey offers an extensive overview of few-shot learning methods, including a detailed discussion of evaluation protocols, benchmark datasets, and open challenges across various domains.