This problem is widely studied in computer vision, natural language processing, and machine perception.
[4] The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS’09.
This approach was also extended to multilingual domains,[8][9] fine entity typing[10] 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[11] and question answering.
In generalized zero-shot learning, samples from both new and known classes, may appear at test time.