  In class incremental learning (CIL) a model must learn new classes in a
sequential manner without forgetting old ones. However, conventional CIL
methods consider a balanced distribution for each new task, which ignores the
prevalence of long-tailed distributions in the real world. In this work we
propose two long-tailed CIL scenarios, which we term ordered and shuffled
LT-CIL. Ordered LT-CIL considers the scenario where we learn from head classes
collected with more samples than tail classes which have few. Shuffled LT-CIL,
on the other hand, assumes a completely random long-tailed distribution for
each task. We systematically evaluate existing methods in both LT-CIL scenarios
and demonstrate very different behaviors compared to conventional CIL
scenarios. Additionally, we propose a two-stage learning baseline with a
learnable weight scaling layer for reducing the bias caused by long-tailed
distribution in LT-CIL and which in turn also improves the performance of
conventional CIL due to the limited exemplars. Our results demonstrate the
superior performance (up to 6.44 points in average incremental accuracy) of our
approach on CIFAR-100 and ImageNet-Subset. The code is available at
https://github.com/xialeiliu/Long-Tailed-CIL
