Zero-Shot Learning

Zero-shot learning in machine learning is the ability of a model to correctly make predictions for classes it has not seen during training. It relies on understanding the relationships between classes.

Zero-Shot Learning in Practice

This approach is particularly useful in scenarios where it's impractical to have labeled data for every category, like in natural language processing or image recognition. Zero-shot learning often involves transfer learning and semantic understanding of data.