# few-shot learning

> machine learning approach that enables a system to learn new tasks or recognize new objects from only a few examples or demonstrations, rather than requiring extensive data

**Wikidata**: [Q110797734](https://www.wikidata.org/wiki/Q110797734)  
**Source**: https://4ort.xyz/entity/few-shot-learning

## Summary
Few‑shot learning is a machine learning approach that lets a system learn new tasks or recognize new objects from only a few examples, instead of needing large amounts of data.

## Key Facts
- It enables a system to learn new tasks from only a few examples.  
- It enables a system to recognize new objects from only a few demonstrations.  
- It works with only a few examples per task.  
- It works with only a few demonstrations per object class.  
- It does not require extensive data for training.  

## FAQs
### Q: Is few‑shot learning a type of machine learning?  
A: Yes, it is a subclass of machine learning.  

### Q: What problem does few‑shot learning address?  
A: It addresses the problem of training models when only a small number of labeled examples are available.  

### Q: How does few‑shot learning differ from traditional approaches?  
A: Traditional approaches typically need large labeled datasets, whereas few‑shot learning succeeds with only a few examples.  

## Why It Matters
Few‑shot learning reduces the data collection burden that hampers many AI projects. By requiring only a handful of examples, it makes it feasible to deploy models in domains where labeling is expensive, time‑consuming, or impractical. This capability expands the reach of machine learning to niche tasks, rare categories, and rapidly changing environments. It also accelerates prototyping, as developers can obtain functional models without assembling massive datasets. Consequently, few‑shot learning is a key technique for making AI more adaptable, cost‑effective, and accessible across diverse applications.

## Notable For
- Learning new tasks from minimal examples.  
- Recognizing new objects with only a few demonstrations.  
- Eliminating the need for extensive labeled datasets.  
- Being classified as a subclass of machine learning.  

## Body
### Definition
Few‑shot learning is a machine learning approach.  
It enables learning of new tasks from a small number of examples.  
It enables recognition of new objects from a small number of demonstrations.  

### Data Efficiency
The approach operates with only a few examples per task.  
It also operates with only a few demonstrations per object class.  
Because of this, it does not require extensive data for training.  

### Relationship to Machine Learning
Few‑shot learning is a subclass of machine learning.  

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "few-shot learning",
  "description": "A machine learning approach that enables a system to learn new tasks or recognize new objects from only a few examples, rather than requiring extensive data.",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q1234567",
    "https://en.wikipedia.org/wiki/Few-shot_learning"
  ],
  "additionalType": "MachineLearningMethod"
}

## References

1. [Source](https://misovalko.github.io/)