Definition
Few-shot learning aims to enable models to generalize from just a handful of examples, mimicking human learning efficiency. It typically relies on meta-learning strategies, where a model is trained on a variety of tasks to acquire the ability to quickly adapt to new tasks with minimal data. This is crucial in domains where labeled data is scarce or expensive to obtain, such as rare disease diagnosis or niche industrial defect detection.
Summary
Few-shot learning is a machine learning paradigm where models learn new concepts from very limited labeled training data.
Key Concepts
- Meta-learning
- Data efficiency
- Generalization
- Task adaptation
Use Cases
- Rare disease identification
- New product categorization
- Custom object detection