Few-Shot Learning

Few-shot learning is a machine learning paradigm that enables a model to learn from only a small number of training examples. This approach is particularly useful in situations where data collection is expensive or time-consuming, allowing models to generalize from limited information. Few-shot learning often utilizes techniques such as meta-learning and transfer learning to enhance performance by leveraging knowledge from related tasks. Common use cases include image classification, natural language understanding, and any scenario where acquiring large datasets is impractical.