Definition
Zero-shot learning is a machine learning technique where a model can recognize objects or concepts it has not been explicitly trained on. This is achieved by leveraging auxiliary information, such as textual descriptions or attributes, to infer relationships between known and unknown categories. The model learns a mapping from the input data to a shared semantic space, allowing it to generalize to new classes based on their descriptions. This capability is crucial for building adaptable AI systems that can handle novel situations without requiring extensive retraining. For instance, a zero-shot learning model could identify a specific breed of dog it has never seen before if it has been trained on general canine features and descriptions. It differs from few-shot learning, which requires a small number of examples for new classes. This concept appears in AI and responsible AI domains, relevant for certifications like AWS Certified Solutions Architect.