open-set classification

Open-set classification is an important task in the field of computer vision, aiming to handle samples of new categories that do not appear in the training set. The goal of this task is to recognize known categories while also detecting and rejecting unknown categories, thereby enhancing the robustness and generalization capabilities of the system. Open-set classification holds significant value in applications such as security monitoring, autonomous driving, and medical image analysis, effectively addressing uncertainties and complexities in the real world.