What is few-shot learning?

Few-shot learning is a machine learning approach that aims to learn from a limited number of training examples. Unlike traditional machine learning models that require vast amounts of data, few-shot learning techniques leverage prior knowledge, meta-learning, or transfer learning to generalize effectively from only a few labeled samples. It is commonly used in scenarios where obtaining large labeled datasets is expensive, time-consuming, or simply not feasible, such as image recognition with rare objects or natural language processing with low-resource languages.