Few-shot learning is a machine learning technique where a model is trained to generalize from only a small number of training examples. Unlike traditional models that require large datasets to learn and make predictions effectively, few-shot learning aims to enable models to perform well on new tasks with minimal data. This approach mimics human learning, where people can often understand new concepts after seeing only a few examples.
Few-shot learning is particularly valuable in scenarios where data is scarce or expensive to acquire, such as in medical imaging, rare language processing, or specialized business use cases. Techniques used in few-shot learning include transfer learning, meta-learning, and other methods designed to extract patterns from limited data and generalize them effectively to new situations.