Category Prototype

Category prototypes are representative examples of a class used in machine learning to improve classification accuracy and generalization, particularly in challenging scenarios like unsupervised domain adaptation and noisy data. Current research focuses on developing algorithms that leverage category prototypes effectively, often integrating them with contrastive learning and contextual information to enhance feature learning and address issues like semantic mismatches and noisy labels. These advancements are improving the performance of various tasks, including image segmentation and generalized category discovery, leading to more robust and accurate models in diverse applications.