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I keep on hearing this term "shot" used in machine learning.

Is a "shot" well-defined?

From what I can tell, "shot" is a synonym for "example". Most machine learning systems seem to be "multi-shot" meaning you have a huge dataset that has many different examples of different categories. However, for a system to have "one-shot" capabilities means that it is able to predict the category of something given exactly one example. Similarly, "few-shot" applications seem to only need a few examples in order to perform some function with the input. And "zero-shot" learning seems to be making predictions without any examples during training.

Is a shot just an example?

Given the evidence above, it seems like this is the case, but it also seems like it's a little bit more nuanced, something like a shot is a post-training example when 0 examples were given during training. But I'm not sure if this is right, thus the question.

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A shot is a single example available for machine learning. So "one-shot" means you're given just the one example for each class.

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