Few-Shot Learning
Few-Shot Learning is a machine learning approach that enables models to learn from a very small number of training examples. Unlike traditional methods that require large datasets, Few-Shot Learning aims to generalize knowledge from just a few instances, making it particularly useful in situations where data is scarce or expensive to obtain.
This technique often employs strategies like meta-learning, where the model learns how to learn from previous tasks. By leveraging prior knowledge, Few-Shot Learning can quickly adapt to new tasks with minimal data, making it valuable in fields such as natural language processing and computer vision.