What is few-shot learning and how does it compare to traditional learning?

What is few-shot learning and how does it compare to traditional learning?

Few-shot learning is a machine learning approach where a model learns from a very small number of training examples. In contrast, traditional learning typically requires a large dataset to achieve good performance.

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

Sure! An example would be training a model to recognize new objects with just a few images of each object, like identifying different dog breeds with only a few pictures of each breed.

What are the advantages of few-shot learning?

The main advantages include reduced data requirements, faster training times, and the ability to generalize better from limited examples.

Are there any limitations to few-shot learning?

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