  Zero-shot learning deals with the ability to recognize objects without any
visual training sample. To counterbalance this lack of visual data, each class
to recognize is associated with a semantic prototype that reflects the
essential features of the object. The general approach is to learn a mapping
from visual data to semantic prototypes, then use it at inference to classify
visual samples from the class prototypes only. Different settings of this
general configuration can be considered depending on the use case of interest,
in particular whether one only wants to classify objects that have not been
employed to learn the mapping or whether one can use unlabelled visual examples
to learn the mapping. This chapter presents a review of the approaches based on
deep neural networks to tackle the ZSL problem. We highlight findings that had
a large impact on the evolution of this domain and list its current challenges.
