Zero-shot Generalization

Zero-shot Generalization refers to the ability of a machine learning model to make accurate predictions on unseen data. Its goal is to enable the model to generalize to entirely new tasks or categories by learning from existing data distributions, without the need for additional training on these new tasks. This capability is of significant application value in enhancing the adaptability of models and reducing the demand for labeled data, especially in scenarios where data is scarce or difficult to obtain.