  Existing Class Incremental Learning (CIL) methods are based on a supervised
classification framework sensitive to data labels. When updating them based on
the new class data, they suffer from catastrophic forgetting: the model cannot
discern old class data clearly from the new. In this paper, we explore the
performance of Self-Supervised representation learning in Class Incremental
Learning (SSCIL) for the first time, which discards data labels and the model's
classifiers. To comprehensively discuss the difference in performance between
supervised and self-supervised methods in CIL, we set up three different class
incremental schemes: Random Class Scheme, Semantic Class Scheme, and Cluster
Scheme, to simulate various class incremental learning scenarios. Besides, we
propose Linear Evaluation Protocol (LEP) and Generalization Evaluation Protocol
(GEP) to metric the model's representation classification ability and
generalization in CIL. Our experiments (on ImageNet-100 and ImageNet) show that
SSCIL has better anti-forgetting ability and robustness than supervised
strategies in CIL. To understand what alleviates the catastrophic forgetting in
SSCIL, we study the major components of SSCIL and conclude that (1) the
composition of different data augmentation improves the quality of the model's
representation and the \textit{Grayscale} operation reduces the system noise of
data augmentation in SSCIL. (2) the projector, like a buffer, reduces
unnecessary parameter updates of the model in SSCIL and increases the
robustness of the model. Although the performance of SSCIL is significantly
higher than supervised methods in CIL, there is still an apparent gap with
joint learning. Our exploration gives a baseline of self-supervised class
incremental learning on large-scale datasets and contributes some forward
strategies for mitigating the catastrophic forgetting in CIL.
