Few-Shot Learning refers to a method in artificial intelligence where an AI model can learn new tasks from just a few examples. Unlike traditional machine learning, which often requires thousands of training samples, Few-Shot Learning can work with just a handful of examples – sometimes as few as two or three.
It can be compared to a particularly gifted student who understands and applies a new concept after just a few explanations. This capability is especially important in situations where training data is limited or where collecting data would be very time-consuming or expensive.
A practical example would be an AI in medical diagnostics: While traditional systems would need thousands of X-ray images of a specific rare disease, a Few-Shot Learning system can recognize this disease in new cases after studying just a few example images.
Few-Shot Learning works on the principle that the AI model already possesses broad foundational knowledge and cleverly applies this to new but related tasks – similar to how humans use their existing knowledge to master new situations.