In machine learning, zero-shot learning refers to the process by which a machine learns how to recognize objects in an image without any labeled training data to help in the classification.
In other words, ZSL aims to help machines categorize objects that they have never seen before.
For example, given a set of images of animals to be classified, along with auxiliary textual descriptions of what animals look like, an AI which has been trained to recognize horses, but has never seen a zebra, can still recognize a zebra if it also knows that zebras look like striped horses.
