Few-shot image classification is the task of classifying unseen images to one of N mutually exclusive classes, using only a small number of training examples for each class. The limited availability of these examples (denoted as K ) presents a significant challenge to classification accuracy in some cases.

What is few-shot learning? Few-shot learning is a machine learning framework in which an AI model learns to make accurate predictions by training on a very small number of labeled examples. It's typically used to train models for classification tasks when suitable training data is scarce.