面向持续学习的自步权重整合方法
Self-paced Weight Consolidation for Continual Learning
AI总结：
针对持续学习中未区分旧任务贡献导致性能下降、正则化所有旧任务计算成本高的问题，提出自步权重整合框架，按准确率衡量旧任务难度并排序，选择性保留难任务知识，可即插即用适配多种算法，在多个基准数据集上有效提升性能。
摘要：
在序列任务学习场景中，通过让新任务的参数与先前任务的参数保持接近的持续学习算法，被广泛用于防止灾难性遗忘问题。然而，这类方法存在两大局限：其一，由于未区分先前已学任务的贡献差异，新型持续学习模型的性能会出现下降；其二，由于大多数现有算法在学习新任务时需要对所有先前任务施加正则化，计算成本会随着任务数量的增加大幅上升。为解决上述挑战，我们提出一种自步权重整合（self-paced Weight Consolidation，spWC）框架，通过评估先前任务的判别性贡献实现鲁棒的持续学习。具体而言，我们设计了一种自步正则化策略，基于关键性能指标（即准确率）衡量任务难度，以此反映过往任务的优先级。当遇到新任务时，所有先前任务会根据优先级从“难”到“易”排序，随后新的持续学习模型参数将通过选择性保留难度更高的过往任务中的知识进行学习，既能有效克服灾难性遗忘，又能降低计算成本。我们采用交替凸搜索方法，在双凸优化形式下迭代更新模型参数和优先级权重。所提出的spWC框架具备即插即用特性，可适配不同方向（如分类、分割）的大多数持续学习算法（如EWC、MAS、RCIL）。在多个公开基准数据集上的实验结果表明，与其他主流持续学习算法相比，本文提出的框架能够有效提升性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
