Semi-supervised learning-open set recognition

Semi-supervised learning - open set recognition is an important task in the field of computer vision, aiming to combine a small amount of labeled data with a large amount of unlabeled data for model training, while effectively identifying new categories not present in the training set. The goal of this task is to improve the model's generalization and robustness, reduce the reliance on large amounts of labeled data, and enhance the system's adaptability and accuracy in real-world applications. Its application value lies in being able to handle new categories that continuously emerge in the real world, thereby increasing the intelligence level of the system.