Few-shot learning is a research area and methodological approach in machine learning concerned with training models to perform tasks or recognize new classes using only a very limited number of labeled examples per task or class. This field investigates techniques that enable models to generalize rapidly and effectively from scarce data, often by leveraging prior knowledge, meta-learning, or learning robust data representations, aiming to achieve performance comparable to models trained on significantly larger datasets.