Deep state-space models (DSSMs) have gained popularity in recent years due to
their potent modeling capacity for dynamic systems. However, existing DSSM
works are limited to single-task modeling, which requires retraining with
historical task data upon revisiting a forepassed task. To address this
limitation, we propose continual learning DSSMs (CLDSSMs), which are capable of
adapting to evolving tasks without catastrophic forgetting. Our proposed
CLDSSMs integrate mainstream regularization-based continual learning (CL)
methods, ensuring efficient updates with constant computational and memory
costs for modeling multiple dynamic systems. We also conduct a comprehensive
cost analysis of each CL method applied to the respective CLDSSMs, and
demonstrate the efficacy of CLDSSMs through experiments on real-world datasets.
The results corroborate that while various competing CL methods exhibit
different merits, the proposed CLDSSMs consistently outperform traditional
DSSMs in terms of effectively addressing catastrophic forgetting, enabling
swift and accurate parameter transfer to new tasks.

深度状态空间模型 (DSSM) 在动态系统建模方面的能力使其在近年来受到广泛关注。然而，现有 DSSM 方法仅适用于单任务建模，需要在重新访问之前的任务时使用历史任务数据进行重新训练。为了解决这个问题，我们提出了一种连续学习 DSSM (CLDSSM) 方法，它能够适应不断变化的任务而不会导致灾难性遗忘。我们的 CLDSSM 方法整合了主流的基于正则化的连续学习方法，确保以恒定的计算和内存成本对多个动态系统进行模型化的高效更新。我们还对各个连续学习方法应用于相应的 CLDSSM 进行了全面的成本分析，并通过在真实数据集上的实验证明了 CLDSSM 的有效性。结果证实，尽管各种竞争的连续学习方法各具特点，但所提出的 CLDSSM 在解决灾难性遗忘方面始终优于传统的 DSSM 方法，并能够实现对新任务的快速准确参数传递。