What is Few-Shot Learning?

Few-Shot Learning is a machine learning approach that enables AI models to learn effectively from only a small number of labeled examples. Unlike traditional models, which require massive datasets and significant computational resources, few-shot learning leverages prior knowledge to make accurate predictions with minimal data.  

It relies on large, trained AI models that have already been trained on massive datasets and now understands broad languages or patterns. The model is given a few examples called shots along with instructions for new task. Using the prior knowledge plus the examples and the instructions it has received model can process the request and offer predictions.

How Few-shot Learning Works

In Few-shot learning, each task is divided into two components: the Support Set and Query Set.

• Support Set: This is a small collection of labeled examples. Each data point in the set comes with its corresponding label, and together they provide the model with the basic knowledge needed to understand the task.
• Query Set: This set contains unlabeled examples. The model’s job is to predict the correct labels for these query samples based on what it has learned from the Support Set.

By comparing the query set with the support sets, the model identifies similarities and assigns labels accordingly. Common similarity measures include Cosine similarity and Euclidean distance.