Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classes while alleviating the catastrophic forgetting. In the self-supervised manner, it becomes more challenging to avoid the conflict between the feature embedding spaces of novel classes and old ones without any class labels. To address the problem, we propose a self-supervised CIL framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the ESR modules reserve embedding space for subsequent phases at the prototype level and the feature level respectively to prepare for knowledge learned in the future. 2) The MTD module maintains the representations of the current phase without the interference of past knowledge. One of the teacher networks retains the representations of the past phases, and the other teacher network distills relation information of the current phase to the student network. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our proposed method boosts the performance of self-supervised class incremental learning. We will release code in the near future.

翻译：类增量学习（CIL）旨在处理新类别数据持续且顺序出现的场景。模型需在缓解灾难性遗忘的同时识别顺序出现的新类别。在无监督条件下，由于缺乏类别标签，避免新旧类别特征嵌入空间之间的冲突变得更加困难。为解决该问题，我们提出自监督类增量学习框架CPPF——即"融合过去、现在与未来"。具体而言，CPPF包含原型聚类模块（PC）、嵌入空间预留模块（ESR）和多教师蒸馏模块（MTD）。1）PC和ESR模块分别从原型层面和特征层面为后续阶段预留嵌入空间，以准备未来学习到的知识；2）MTD模块在当前阶段维持表征能力且不受过去知识的干扰，其中一个教师网络保留过去阶段的表征，另一个教师网络将当前阶段的关联信息蒸馏至学生网络。在CIFAR100和ImageNet100数据集上的大量实验表明，所提方法有效提升了自监督类增量学习的性能。相关代码将在近期开源。