Combining Past, Present and Future: A Self-Supervised Approach for Class
  Incremental Learning
Xiaoshuang Chen Zhongyi Sun Ke Yan Shouhong Ding Hongtao Lu
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.