Few-Shot Learning

Few-shot learning trains models with minimal examples (5-50 per class) instead of thousands. Achieve 80-90% accuracy using meta-learning, prototypical networks, and transfer learning - critical when data is scarce or expensive.

Key Approaches

1. Meta-Learning (Learning to Learn)

• MAML (Model-Agnostic Meta-Learning): Learn initialization that adapts quickly
• Train on many tasks, few examples each
• Fine-tune on new task with 5-10 examples
• 80-85% accuracy vs 60-70% without meta-learning

2. Prototypical Networks

• Learn embedding space where similar classes cluster
• Classify based on distance to class prototypes
• Simple, effective, widely used

3. Matching Networks

• Attention-based matching to support examples
• Non-parametric approach
• Good for one-shot (single example) scenarios

Applications

• Medical Imaging: Rare diseases (limited training data)
• Manufacturing: New defect types
• NLP: Low-resource languages
• Personalization: User-specific models with minimal data

Results

• 5-shot learning: 75-85% accuracy (vs 40-60% standard training)
• 10-shot: 80-90% accuracy
• 100x less data needed vs traditional training