Few-shot learning—the ability to learn from very small numbers of examples—represents a significant challenge in machine learning. This article examines progress in this important area.

We'll look at techniques that enable models to generalize effectively from just a handful of examples, moving closer to human-like learning capabilities.

Advances in this field could dramatically expand the applicability of machine learning to new domains.