Ever wondered what happens if we give instruction to a tool to "Write an article about zero-shot learning" and the tool generates an entire article about it? Now imagine that nowhere in the dataset which was used to train the model, do we have anything which has the set used to train this answer. That's zero-shot learning.
Zero-shot learning is a machine learning technique where models can learn to classify data points without any labels for those points. This is possible because the model can learn to generalize from other data points that are similar to the ones it needs to classify.
Zero-shot learning is especially useful when there is a limited amount of labelled data available, or when the data points to be classified are very different from the ones that were used to train the model. In these cases, it would be very difficult, if not impossible, to get enough labelled data to train a traditional machine learning model.
There are a few different approaches to zero-shot learning. One popular approach is to learn a mapping from the data points to a latent space, and then train a classifier in that latent space. Another approach is to learn a classifier directly from the data, without using a latent space.
Zero-shot learning is a relatively new technique, and there is still much research being done to improve it. However, it has already shown to be promising and has been used in a variety of applications such as image classification, object detection, and text classification.
At the AAAI'08 conference in 2008, the first paper on zero-shot learning for natural language processing was presented, but the learning paradigm there was referred to as dataless classification.