Zero-shot learning (ZSL) is a machine learning approach where a model correctly predicts classes it has never seen during training, using semantic knowledge (e.g., descriptions, attributes, embeddings) to transfer understanding from known classes to unseen ones.
Example: A model trained on animals (dog, cat) can identify a zebra by leveraging textual descriptions like “striped horse-like animal.”