Learning from a few data points is called few-shot learning or k-shot learning, where k specifies the number of data points in each of the class in the dataset.
Consider we are performing an image classification task. Say we have two classes – apple and orange – and we try to classify the given image as an apple or orange. When we have exactly one apple and one orange image in our training set, it is called one-shot learning; that is, we are learning from just one data point per each of the class. If we have, say, 11 images of an apple and 11 images of an orange, then that is called 11-shot learning. So, k in k-shot learning implies the number of data points we have per class.
There is also zero-shot learning, where we don't have any data points per class. Wait. What? How can we learn when there are no data points at all? In this case...
