Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost will be greatly increased with the number of tasks, since most existing algorithms need to regularize all previous tasks when learning new tasks. To address the above challenges, we propose a self-paced Weight Consolidation (spWC) framework to attain robust continual learning via evaluating the discriminative contributions of previous tasks. To be specific, we develop a self-paced regularization to reflect the priorities of past tasks via measuring difficulty based on key performance indicator (i.e., accuracy). When encountering a new task, all previous tasks are sorted from "difficult" to "easy" based on the priorities. Then the parameters of the new continual learner will be learned via selectively maintaining the knowledge amongst more difficult past tasks, which could well overcome catastrophic forgetting with less computational cost. We adopt an alternative convex search to iteratively update the model parameters and priority weights in the bi-convex formulation. The proposed spWC framework is plug-and-play, which is applicable to most continual learning algorithms (e.g., EWC, MAS and RCIL) in different directions (e.g., classification and segmentation). Experimental results on several public benchmark datasets demonstrate that our proposed framework can effectively improve performance when compared with other popular continual learning algorithms.

翻译：持续学习算法通过将新任务的参数保持与先前任务相近，在顺序任务学习场景中有效防止灾难性遗忘。然而，现有方法存在以下问题：1) 由于未区分先前学习任务的贡献差异，新持续学习器的性能会下降；2) 学习新任务时需正则化所有先前任务，导致计算成本随任务数量显著增加。针对上述挑战，我们提出自我调节权重巩固（spWC）框架，通过评估先前任务的判别性贡献实现鲁棒持续学习。具体而言，我们开发了一种基于关键绩效指标（即准确率）进行难度测量的自我调节正则化方法，以反映先前任务的优先级。当遇到新任务时，所有先前任务依据优先级从"困难"到"容易"排序。随后，新持续学习器的参数通过选择性保留较困难先前任务的知识进行学习，从而以更低计算成本有效克服灾难性遗忘。我们采用交替凸搜索方法，在双凸优化框架下迭代更新模型参数与优先级权重。所提出的spWC框架具有即插即用特性，可适用于不同方向（如分类与分割）的大多数持续学习算法（例如EWC、MAS和RCIL）。在多个公开基准数据集上的实验结果表明，相比其他主流持续学习算法，本框架能有效提升性能。