# Precise description of one-shot learning

I am working on classifying the Omniglot dataset, and the different papers dealing with this topic describe the problem as one-shot learning (classification). I would like to nail down a precise description of what counts as one-shot learning.

It's clear to me that in one-shot classification, a model tries to classify an input into one of $$C$$ classes by comparing it to exactly one example from each of the $$C$$ classes.

What I want to understand is:

1. Is it necessary that the model has never seen the input and the target examples before, for the problem to be called one-shot?

2. Goodfellow et. al. describe one-shot learning as an extreme case transfer learning where only one labeled example of the transfer task is presented. So, it means they are considering the training process as a kind of continuous transfer learning? What has the model learned earlier, that is being transferred?