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.
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