  Although the concept of catastrophic forgetting is straightforward, there is
a lack of study on its causes. In this paper, we systematically explore and
reveal three causes for catastrophic forgetting in Class Incremental
Learning(CIL). From the perspective of representation learning,(i) intra-phase
forgetting happens when the learner fails to correctly align the same-phase
data as training proceeds and (ii) inter-phase confusion happens when the
learner confuses the current-phase data with the previous-phase. From the
task-specific point of view, the CIL model suffers from the problem of (iii)
classifier deviation. After investigating existing strategies, we observe that
there is a lack of study on how to prevent the inter-phase confusion. To
initiate the research on this specific issue, we propose a simple yet effective
framework, Contrastive Class Concentration for CIL (C4IL). Our framework
leverages the class concentration effect of contrastive learning, yielding a
representation distribution with better intra-class compactibility and
inter-class separability. Empirically, we observe that C4IL significantly
lowers the probability of inter-phase confusion and as a result improves the
performance on multiple CIL settings of multiple datasets.
