What is zero-shot learning

Zero-shot learning is a machine learning technique enabling models to recognize and classify objects or concepts they have never encountered during training. It achieves this by leveraging semantic relationships and auxiliary information to generalize from seen to unseen categories.

This approach requires accessible semantic descriptions like attributes or text embeddings that characterize seen and unseen classes. Models learn a shared embedding space aligning input features with semantic vectors. Its feasibility hinges on the quality and relevance of these semantic links and the model's ability to infer beyond its training data. Key techniques include attribute-based classification and embedding-based projection.

The primary application lies in scenarios lacking labeled data for all possible categories, common in image recognition, natural language processing, and multimodal systems. Its key value is dramatically reducing the need for extensive labeled datasets, enabling the identification of rare or novel classes and facilitating flexible AI systems. This is vital for real-world adaptability.