Few-Shot Learning

Few-Shot Learning (FSL) is a machine learning approach that aims to develop models capable of learning new concepts or tasks with a very limited amount of training data. Unlike traditional machine learning methods that require large datasets to generalize well, few-shot learning seeks to enable models to make accurate predictions based on just a handful of examples, often referred to as "shots."FSL is particularly useful in scenarios where data is scarce or expensive to obtain, such as in medical imaging or rare events classification. It often employs techniques such as meta-learning, where a model learns to learn from different tasks, enabling it to quickly adapt to new tasks with minimal examples. There are a variety of strategies within few-shot learning, including prototype networks, matching networks, and transfer learning approaches, which focus on maximizing the information gained from limited data points. The goal is to enhance the model's ability to generalize and perform well in new situations with very few training instances.