Zero-shot learning

Zero-shot learning (ZSL) is a machine learning approach where a model is able to make predictions on classes or categories that it has never encountered during training. In traditional supervised learning, the model learns to classify based on labeled examples of each class. In contrast, zero-shot learning aims to generalize knowledge from seen classes to unseen classes based on some form of auxiliary information, such as attributes, class descriptions, or relationships.