提出任务序列可迁移性度量，优化持续学习任务顺序。
Sequence Transferability and Task Order Selection in Continual Learning
- 定义前向与后向可迁移性度量，量化任务序列影响。
- 新方法在准确率上优于随机任务顺序选择。
- 适合需要高效任务排序的持续学习场景。
在持续学习中，理解任务序列的特性及其对模型性能的影响至关重要，有助于开发更优的算法。然而，尽管方法论取得了进展，相关研究仍不充分。本文研究了任务序列可迁移性对持续学习的影响，提出了两种新度量方法，分别捕捉任务序列在正向和反向上的总可迁移性。基于这些度量的实证特性，我们进一步提出一种新的任务顺序选择方法。实验表明，该方法在性能上优于传统的随机任务选择策略。
原文摘要 · Abstract (English)
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.
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