Few Shot Class Incremental Learning

Few-Shot Class-Incremental Learning is a machine learning approach aimed at incrementally learning new categories with a few samples to continuously expand the model's classification capabilities. The goal of this method is to efficiently adapt to new category data without forgetting existing knowledge, thereby enhancing the model's generalization and real-time updating abilities. Its application value lies in effectively addressing issues of data distribution changes and the emergence of new categories in the real world, making it suitable for continuous learning tasks in dynamic environments.