Zero-shot learning

Zero-shot learning (ZSL) is a problem setup in <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a> where, at test time, a learner observes samples from classes which were not observed during <a href="/facts/Machine_learning/e0w0XJTu">training</a>, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of <a href="/facts/One-shot_learning_in_computer_vision/CgIw6JSz">one-shot learning</a>, in which classification can be learned from only one, or a few, examples.
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 artificial intelligence model which has been trained to recognize horses, but has never been given a zebra, can still recognize a zebra when it also knows that zebras look like striped horses. This problem is widely studied in <a href="/facts/Computer_vision/Tl2Yyk66">computer vision</a>, <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a>, and <a href="/facts/Machine_perception/2sQmptrY">machine perception</a>.

Zero-shot learning open-in-new

Zero-shot learning