zero-shot

Concept in machine learning where a model can make predictions on tasks it has never encountered before. This is achieved by leveraging knowledge from related tasks or domains, often using semantic information like word embeddings or attribute descriptions. Zero-shot learning is particularly useful for scenarios where collecting labeled data for every possible task is impractical, benefiting researchers and developers in fields such as natural language processing and computer vision.