The dynamic nature of open-world scenarios has attracted more attention to class incremental learning (CIL). However, existing CIL methods typically presume the availability of complete ground-truth labels throughout the training process, an assumption rarely met in practical applications. Consequently, this paper explores a more challenging problem of unsupervised class incremental learning (UCIL). The essence of addressing this problem lies in effectively capturing comprehensive feature representations and discovering unknown novel classes. To achieve this, we first model the knowledge of class distribution by exploiting fine-grained prototypes. Subsequently, a granularity alignment technique is introduced to enhance the unsupervised class discovery. Additionally, we proposed a strategy to minimize overlap between novel and existing classes, thereby preserving historical knowledge and mitigating the phenomenon of catastrophic forgetting. Extensive experiments on the five datasets demonstrate that our approach significantly outperforms current state-of-the-art methods, indicating the effectiveness of the proposed method.

本研究针对现有类增量学习方法假设训练过程中具备完整标签这一现实中的缺口，探讨了无监督类增量学习的问题。通过利用细粒度原型建模类别分布及引入粒度对齐技术，显著提升了无监督类发现的效果，并提出了减少新旧类别重叠的策略，以保留历史知识并减轻灾难性遗忘现象。实验结果表明，所提方法在多个数据集上显著优于现有最先进的方法。

利用细粒度原型分布提升无监督类增量学习