Zero-shot learning

Zero-shot learning (ZSL) is a machine learning technique where a model can recognize and classify data it has never encountered before. This is achieved by leveraging semantic information about the classes, such as textual descriptions or attributes, to make predictions about new, unseen classes. Zero-shot learning is particularly useful for tasks where it is impractical to collect and label data for every possible class, benefiting fields like image recognition and natural language processing.