Open World Semi Supervised Learning

Open-World Semi-Supervised Learning is a machine learning approach designed to handle the presence of unlabelled data and unknown categories, enhancing the model's generalization ability by leveraging a small amount of labelled data and a large amount of unlabelled data. This method not only extends the boundaries of traditional semi-supervised learning but also enables the recognition and handling of new categories in an open environment, improving the system's robustness and adaptability. In the field of computer vision, this approach helps address issues such as high data annotation costs and uneven category distribution, thereby increasing the practical value of models in real-world scenarios.