Zero, Single, and Few-shot learning are three variations of the same concept – providing a model with very little training data to make predictions about new data. In this field, a "shot" refers to a single training example, thereby indicating how challenging these techniques can be. While it might seem challenging to train a model with so little data, these techniques hold great promise in industries where data is scarce, such as in medical research, speech recognition, and more.
