  Continual learning (CL) is an approach to address catastrophic forgetting,
which refers to forgetting previously learned knowledge by neural networks when
trained on new tasks or data distributions. The adversarial robustness has
decomposed features into robust and non-robust types and demonstrated that
models trained on robust features significantly enhance adversarial robustness.
However, no study has been conducted on the efficacy of robust features from
the lens of the CL model in mitigating catastrophic forgetting in CL. In this
paper, we introduce the CL robust dataset and train four baseline models on
both the standard and CL robust datasets. Our results demonstrate that the CL
models trained on the CL robust dataset experienced less catastrophic
forgetting of the previously learned tasks than when trained on the standard
dataset. Our observations highlight the significance of the features provided
to the underlying CL models, showing that CL robust features can alleviate
catastrophic forgetting.
