What is few-shot learning in AI?

What is few-shot learning in AI?

Few-shot learning is a machine learning approach where a model learns from a very small number of training examples.

How does it differ from traditional machine learning?

Traditional machine learning often requires large datasets to train effectively, while few-shot learning aims to generalize from just a few examples.

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

Sure! An example is training a model to recognize a new object with only a few images of it, like identifying a new species of flower.

What are the challenges associated with few-shot learning?

Challenges include overfitting, where the model learns noise instead of the signal, and the difficulty in generalizing from limited data.