Zero-shot learning (ZSL) is a technique used in machine learning that enables models to perform tasks despite having no labeled data for those specific tasks. Instead of relying solely on examples within the target task, zero-shot learning leverages knowledge from other related tasks or domains. Essentially, it builds a bridge between known and unknown classes by using additional information, often in the form of semantic attributes or external data sources. For instance, if a model has been trained to recognize animals like cats and dogs, it can apply its existing knowledge of animal features to identify a class it has never seen before, like a zebra, based on shared attributes like 'striped' or 'four-legged'.
To implement zero-shot learning, developers often utilize a two-step approach. First, the model learns a representation of categories using seen data. This is done by associating classes with descriptive attributes or textual information. In our earlier example, one could represent different animals using attributes such as "has stripes," "domestic," or "carnivorous." In the second step, when the model encounters a new class lacking labeled data—for instance, an animal like a zebra—it uses its understanding of the attributes and the relationships between known classes to make an inference. This enables the model to recognize the zebra by matching its attributes with those learned from cats and dogs.
A practical application of zero-shot learning can be found in image classification or natural language processing tasks. For example, consider an image classification system that has been trained on various types of vehicles—cars, buses, and bikes. If one needs to identify a new category like electric scooters, which the model has never explicitly seen, the model can classify these vehicles based on related attributes such as "two-wheeled" and "electric." Similarly, in NLP, a model can interpret the sentiment of text in a new domain, like customer reviews of a product, using understanding gained from previously labeled sentiment data in other contexts. This flexibility makes zero-shot learning particularly useful in fields where acquiring labeled data can be resource-intensive or infeasible.