What is a "shot" in machine learning? I keep on hearing this term &quot;shot&quot; used in machine learning.
Is a &quot;shot&quot; well-defined?
From what I can tell, &quot;shot&quot; is a synonym for &quot;example&quot;. Most machine learning systems seem to be &quot;multi-shot&quot; meaning you have a huge dataset that has many different examples of different categories. However, for a system to have &quot;one-shot&quot; capabilities means that it is able to predict the category of something given exactly one example. Similarly, &quot;few-shot&quot; applications seem to only need a few examples in order to perform some function with the input. And &quot;zero-shot&quot; 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.
A shot is a single example available for machine learning. So &quot;one-shot&quot; means you're given just the one example for each class.