Regularization-Based Efficient Continual Learning in Deep State-Space
  Models
Yuanhang Zhang Zhidi Lin Yiyong Sun Feng Yin Carsten Fritsche
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.