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.

翻译：持续学习是一种应对灾难性遗忘的方法，灾难性遗忘指神经网络在新任务或新数据分布上进行训练时，会遗忘先前学到的知识。对抗鲁棒性将特征分解为鲁棒特征和非鲁棒特征，并表明基于鲁棒特征训练的模型能显著增强对抗鲁棒性。然而，目前尚无研究从持续学习模型的角度探究鲁棒特征在缓解灾难性遗忘方面的有效性。本文引入持续学习鲁棒数据集，并在标准数据集和持续学习鲁棒数据集上训练四种基线模型。结果表明，与标准数据集相比，在持续学习鲁棒数据集上训练的模型对先前任务的遗忘程度更低。我们的观察结果凸显了为底层持续学习模型提供特征的重要性，表明持续学习鲁棒特征能够缓解灾难性遗忘。