Definition
Few-shot learning enables machine learning models to generalize from very limited data, typically ranging from one to ten examples per class. Unlike traditional supervised learning which requires thousands of samples, few-shot methods leverage pre-trained knowledge or meta-learning strategies to adapt quickly to new tasks. This capability is crucial for real-world applications where collecting large annotated datasets is expensive, time-consuming, or impossible due to privacy constraints.
Summary
A learning paradigm where a model performs a task correctly after being exposed to only a small number of labeled examples.
Key Concepts
- meta-learning
- generalization
- label efficiency
- pre-training
Use Cases
- Rare disease diagnosis
- Custom intent recognition in chatbots
- Domain adaptation with limited data