One-Shot Learning

One-shot learning refers to an artificial intelligence system’s ability to correctly identify or handle a task after being shown just one example during inference or training.

The term “one-shot” highlights that the AI only needs a single instance to learn how to perform a specific task. This is similar to how humans often learn a new skill or concept after seeing just one demonstration.

• Allows AI models to generalise from a minimal amount of data.
• It differs from zero-shot learning, which uses no examples.
• Typical applications include image recognition, natural language classification, and personalisation.
• It is beneficial in domains where acquiring large datasets is impractical.

One-shot learning is essential to human-like learning efficiency, enabling AI systems to perform well with limited input.