Hands-On Meta Learning with Python

Meta learning and few-shot

Learning from fewer data points is called few-shot learning or k-shot learning where k denotes the number of data points in each of the classes in the dataset. Let's say we are performing the image classification of dogs and cats. If we have exactly one dog and one cat image then it is called one-shot learning, that is, we are learning from just one data point per class. If we have, say 10 images of a dog and 10 images of a cat, then that is called 10-shot learning. So k in k-shot learning implies a 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, we will not have ...