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

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

Few-shot learning involves training a model with a small number of examples, while zero-shot learning allows a model to make predictions on classes it hasn't seen during training.

Can you give me an example of each?

Sure! In few-shot learning, if you have 5 images of a cat, the model learns to recognize cats from just those few examples. In zero-shot learning, the model might identify a cat based on its understanding of animals, even if it has never seen a cat before.

Which one is more effective in real-world applications?

It depends on the context. Few-shot learning is often more effective when you have limited data for specific tasks, while zero-shot learning is useful for tasks where new categories frequently emerge.

Are there any limitations to these learning methods?

Yes, few-shot learning can struggle with generalization if the examples are not diverse enough, and zero-shot learning may fail if the model lacks sufficient knowledge about the unseen classes.