Few-Shot Learning

Definition

Few-shot learning aims at reducing the amount of labeled data required for training by leveraging prior knowledge obtained from similar tasks; this approach is particularly useful when acquiring large datasets is impractical or costly.

When Few-Shot Learning is used

Siamese networks

Which positions need this?

Machine Learning Engineers

Problem

Scarcity of labeled examples hampers effective model training.

Example of how Few-Shot Learning is used in AI

An image recognition system trained on only a few pictures of new objects successfully categorizes them correctly after being shown just one example each during testing phase.