Few-shot learning (FSL)

In natural language processing, in-context learning, few-shot learning or few-shot prompting is a prompting technique that allows a model to process examples before attempting a task. One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.