持续学习中的序列迁移性与任务顺序选择
Sequence Transferability and Task Order Selection in Continual Learning
AI总结：
本研究针对持续学习中任务序列属性研究不足的问题，提出正向和反向总迁移性度量指标，据此开发新的任务顺序选择方法，其性能优于随机选择策略。
摘要：
在持续学习中，理解任务序列的属性及其与模型性能的关系，对于开发具有更高准确率的先进算法至关重要。然而，尽管方法学发展取得了令人鼓舞的进展，该方向的研究仍显不足。本研究调查了序列迁移性对持续学习的影响，并提出两种新颖的度量指标，分别捕捉任务序列在正向或反向方向上的总迁移性。基于这些度量指标的经验属性，我们进而针对持续学习中的任务顺序选择问题开发了一种新方法。该方法的性能优于传统的随机任务选择策略。
英文摘要：
In continual learning, understanding the properties of task sequences and their relationships to model performance is important for developing advanced algorithms with better accuracy. However, efforts in this direction remain underdeveloped despite encouraging progress in methodology development. In this work, we investigate the impacts of sequence transferability on continual learning and propose two novel measures that capture the total transferability of a task sequence, either in the forward or backward direction. Based on the empirical properties of these measures, we then develop a new method for the task order selection problem in continual learning. Our method can be shown to offer a better performance than the conventional strategy of random task selection.
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。
