鲁棒特征在缓解灾难性遗忘中的重要性
The Importance of Robust Features in Mitigating Catastrophic Forgetting
AI总结：
本文从持续学习视角研究鲁棒特征对缓解灾难性遗忘的作用，通过引入CL鲁棒数据集并训练四个基线模型，发现基于CL鲁棒特征训练的模型能显著减少对先前任务的遗忘。
摘要：
持续学习（Continual learning, CL）是一种应对灾难性遗忘的方法，灾难性遗忘是指神经网络在基于新任务或数据分布进行训练时遗忘先前学到的知识。对抗鲁棒性将特征分解为鲁棒和非鲁棒类型，并证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CL模型的视角探讨鲁棒特征在缓解CL中灾难性遗忘方面的有效性。本文引入了CL鲁棒数据集，并在标准数据集和CL鲁棒数据集上训练了四个基线模型。结果表明，在CL鲁棒数据集上训练的CL模型，相较于在标准数据集上训练，对先前学习任务的灾难性遗忘更少。我们的观察强调了提供给底层CL模型的特征的重要性，表明CL鲁棒特征能够缓解灾难性遗忘。
英文摘要：
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.
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