Zero-shot learning

Zero-shot learning (ZSL) is a problem setup in machine learning, where at test time, a learner observes samples from classes that were not observed during training, and needs to predict the class they belong to. Zero-shot methods generally work by associating observed and non observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. 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. This problem is widely studied in computer vision, natural language processing, and machine perception.
Background and history.
The first paper on zero-shot learning in natural language processing appeared in AAAI’08, but the name given to the learning paradigm there was "dataless classification". The first paper on zero-shot learning in computer vision appeared at the same conference, under the name "zero-data learning". This direction was popularized later in another, more well-known, CV paper and the term "zero-shot learning" caught up, as a take-off on "one-shot learning" that was introduced in computer vision years earlier.
In computer vision, zero-shot learning models learned parameters for seen classes along with their class representations and rely on representational similarity among class labels so that, during inference, instances can be classified into new classes.
In natural language processing, the key technical direction developed builds on the ability to "understand the labels"—represent the labels in the same semantic space as that of the documents to be classified. This supports the classification of a "single example" without observing any annotated data, the purest form of zero-shot classification. The original paper made use of the Explicit Semantic Analysis (ESA) representation but later papers made use of other representations, including dense representations. This approach was also extended to multilingual domains, fine entity typing and other problems. Moreover, beyond relying solely on representations, the computational approach has been extended to depend on transfer from other tasks, such as textual entailment and question answering.
The original paper also points out that, beyond the ability to classify a single example, when a collection of examples is given, with the assumption that they come from the same distribution, it is possible to bootstrap the performance in a semi-supervised like manner (or transudative learning).  
Unlike standard generalization in machine learning, where classifiers are expected to correctly classify new samples to classes they have already observed during training, in ZSL, no samples from the classes have been given during training the classifier. It can therefore be viewed as an extreme case of domain adaptation.
Prerequisite information for zero-shot classes.
Naturally, some form of auxiliary information has to be given about these zero-shot classes, and this type of information can be of several types. 
Generalized zero-shot learning.
The above ZSL setup assumes that at test time, only zero-shot samples are given, namely, samples from new unseen classes. In generalized zero-shot learning, samples from both new and known classes, may appear at test time. This poses new challenges for classifiers at test time, because it is very challenging to estimate if a given sample is new or known. Few approaches to handle this include: 
Domains of application.
Zero shot learning has been applied to the following fields:
Zero-shot learning computer programs, some use Wikipedia to represent Category Vectors. A program parses wiki articles and forms numbers based on the words found in that file—words are picked and form categories to sort test data in machine learning.