🎓 Meta Learning
Learn to learn: Train models that rapidly adapt to new tasks
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0 / 5 completedIntroduction to Meta Learning
🎯 What is Meta Learning?
Meta-learning, or "learning to learn," trains models to quickly adapt to new tasks using minimal data by learning from a diverse set of related tasks during meta-training.
Learn general learning strategies that transfer across tasks
🔍 Few-Shot Learning Problem
Train models to recognize new classes with only a few labeled examples per class.
1-Shot Learning
One example per class
5-Shot Learning
Five examples per class
N-Way K-Shot
K examples, N classes
🌟 Why Meta Learning?
Data Efficiency
Learn from few examples, crucial when data is scarce or expensive
Fast Adaptation
Quickly adapt to new tasks without extensive retraining
Generalization
Transfer knowledge across diverse but related tasks
Robustness
Learn invariant features that work across task variations
📚 Meta Learning Framework
Meta-Training (Outer Loop)
Sample diverse tasks from task distribution, optimize meta-parameters
Task Learning (Inner Loop)
Adapt to specific task using support set (training examples)
Meta-Testing
Evaluate on completely new tasks from same distribution
🎲 Task Terminology
Support Set
TrainingFew labeled examples for task adaptation (K examples × N classes)
Query Set
TestingUnlabeled examples to test task performance after adaptation
Episode
UnitSingle task sampled during meta-training (support + query)
🏆 Applications
Computer Vision
Few-shot image classification, object detection with limited data
NLP
Low-resource language tasks, intent classification, translation
Robotics
Quick adaptation to new environments and manipulation tasks
Drug Discovery
Predict molecular properties with limited labeled compounds