One-shot learning (computer vision)

One-shot learning is a problem setup in machine learning which leverages a singular example to assist with classification, originating in the field of computer vision to assist with object categorization. Whereas most machine learning classification methods require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one example. Few-shot learning is used for similar methods which utilize more than one example to assist with classification.