Generalized Few Shot Learning

Generalized Few-Shot Learning is a machine learning approach aimed at achieving rapid recognition and classification of new categories through a small number of samples. This method not only focuses on known categories but also emphasizes the generalization ability to unknown categories, thereby enhancing the adaptability and robustness of the model. Its core objective is to build intelligent systems that can learn effectively in data-scarce scenarios, with wide applications in image recognition, natural language processing, and other fields, making it of significant practical value.