unsupervised class-incremental learning

Unsupervised Class-Incremental Learning is a machine learning approach designed to handle the continuous emergence of new class data without the need for labeling, enabling the model to dynamically expand its classification capabilities. This method achieves self-adaptive learning of feature representations for new classes, thereby significantly enhancing the model's practicality and generalization performance in fields such as computer vision. Its core objective is to ensure the continuous evolution of the model, maintaining high recognition accuracy even when confronted with unknown classes, which holds significant application value.