Few-shot learning is a specialized subfield within machine learning, emerging from the broader domain of supervised learning. It focuses on tackling the significant challenge of training models to make precise predictions or classifications with only a scarce amount of labeled data. In contrast to traditional machine learning approaches that generally rely on extensive datasets for training, few-shot learning delves into techniques that empower models to learn efficiently from just a handful of examples.
