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
The Meta-Learning Problem Formulation
Was this section helpful?
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 Proceedings of the 34th International Conference on Machine Learning, Vol. 70 (PMLR) DOI: 10.48550/arXiv.1703.03400 - Presents a general algorithm for meta-learning by learning a good initial set of parameters that can quickly adapt to new tasks with only a few gradient steps. It articulates the inner and outer loop optimization.
- Prototypical Networks for Few-Shot Learning, Jake Snell, Kevin Swersky, Richard Zemel, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) DOI: 10.48550/arXiv.1703.05175 - Introduces a metric-based meta-learning approach where classification is performed by computing distances to prototype representations of each class, derived from support set examples. This illustrates the use of support and query sets.
- Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas, 2016 Advances in Neural Information Processing Systems, Vol. 29 (NeurIPS) DOI: 10.48550/arXiv.1606.04474 - Proposes learning an optimizer (meta-learner) using a neural network, demonstrating the 'learning to learn' concept in a deep learning context. It frames the meta-learning problem in terms of optimizing an optimization process.