What is the difference between few-shot learning and traditional learning?

What is the difference between few-shot learning and traditional learning?

Few-shot learning is a machine learning approach that learns from a small number of examples, while traditional learning typically requires a large dataset to train models effectively.

Can you give me an example of few-shot learning in action?

Sure! An example of few-shot learning is training a model to recognize new objects with just a few images of each object, like identifying different dog breeds from only a couple of pictures.

What are the advantages of few-shot learning?

The main advantages include reduced data requirements, faster training times, and the ability to generalize from limited examples, making it useful in scenarios where data is scarce.

Are there any limitations to few-shot learning?

Yes, few-shot learning can struggle with complex tasks or when the few examples provided are not representative of the broader category.