The document discusses one-shot learning, which uses prior knowledge to classify new categories of objects with only a single or small number of examples. It explains that one-shot learning incorporates prior information about object categories to update knowledge into a posterior given new observations. Performance results on face and motorbike models show that algorithms using one-shot learning can classify new categories with only 1-5 training images and have error rates between 5.6-15%