Existing research on continual learning (CL) of a sequence of tasks focuses mainly on dealing with catastrophic forgetting (CF) to balance the learning plasticity of new tasks and the memory stability of old tasks. However, an ideal CL agent should not only be able to overcome CF, but also encourage positive forward and backward knowledge transfer (KT), i.e., using the learned knowledge from previous tasks for the new task learning (namely FKT), and improving the previous tasks' performance with the knowledge of the new task (namely BKT). To this end, this paper first models CL as an optimization problem in which each sequential learning task aims to achieve its optimal performance under the constraint that both FKT and BKT should be positive. It then proposes a novel Enhanced Task Continual Learning (ETCL) method, which achieves forgetting-free and positive KT. Furthermore, the bounds that can lead to negative FKT and BKT are estimated theoretically. Based on the bounds, a new strategy for online task similarity detection is also proposed to facilitate positive KT. To overcome CF, ETCL learns a set of task-specific binary masks to isolate a sparse sub-network for each task while preserving the performance of a dense network for the task. At the beginning of a new task learning, ETCL tries to align the new task's gradient with that of the sub-network of the previous most similar task to ensure positive FKT. By using a new bi-objective optimization strategy and an orthogonal gradient projection method, ETCL updates only the weights of previous similar tasks at the classification layer to achieve positive BKT. Extensive evaluations demonstrate that the proposed ETCL markedly outperforms strong baselines on dissimilar, similar, and mixed task sequences.

翻译：现有关于任务序列持续学习的研究主要集中于应对灾难性遗忘，以平衡新任务的学习可塑性与旧任务的记忆稳定性。然而，理想的持续学习智能体不仅应能克服灾难性遗忘，还应促进正向的前向与后向知识传递，即利用先前任务习得的知识辅助新任务学习（称为前向知识传递），并借助新任务知识提升先前任务的性能（称为后向知识传递）。为此，本文首先将持续学习建模为优化问题，其中每个顺序学习任务在保证前向与后向知识传递均为正向的约束下，追求实现其最优性能。继而提出一种新颖的增强型任务持续学习方法，该方法能实现无遗忘与正向知识传递。进一步，本文从理论上推导了可能导致负向前向与后向知识传递的边界条件。基于此边界，提出一种新的在线任务相似性检测策略以促进正向知识传递。为克服灾难性遗忘，该方法通过学习一组任务特定的二进制掩码，为每个任务隔离出一个稀疏子网络，同时保留该任务在稠密网络中的性能。在新任务学习初始阶段，该方法通过将新任务梯度与先前最相似任务的子网络梯度对齐，确保正向的前向知识传递。通过采用新的双目标优化策略与正交梯度投影方法，该方法仅更新分类层中先前相似任务的权重，以实现正向的后向知识传递。大量实验评估表明，所提方法在相异、相似及混合任务序列上均显著优于现有强基线方法。