What is zero-shot learning?

Zero-shot learning is being able to solve a task despite not having received any training examples of that task. For a concrete example, imagine recognizing a category of object in photos without ever having seen a photo of that kind of object before. If you’ve read a very detailed description of a cat, you might be able to tell what a cat is in a photograph the first time you see it.

We consider the problem of zero-shot learning, where the goal is to learn a classifier

f : X → Y that must predict novel values of Y that were omitted from

the training set. To achieve this, we define the notion of a semantic output code

classifier (SOC) which utilizes a knowledge base of semantic properties of Y to

extrapolate to novel classes
https://www.quora.com/What-is-zero-shot-learning
http://www.cs.cmu.edu/afs/cs/project/theo-73/www/papers/zero-shot-learning.pdf