Few-Shot Learning

Few-shot learning is a machine learning approach that enables AI models to learn new concepts from only a small number of examples — typically 1 to 10 — rather than the hundreds or thousands traditionally required. This is achieved through techniques like meta-learning, where models are trained to learn how to learn, and metric learning approaches like Prototypical Networks that compare new examples against learned prototypes. Few-shot learning is essential for accessible teachable AI systems because it minimizes the effort required from users with disabilities to personalize applications, making it practical for a blind person to teach an object recognizer with just a few short videos rather than collecting extensive training datasets.