Comparison of Catastrophic Forgetting Mitigation Strategies in Continual Learning Systems
Vanaparthi Kiranmai,
Krishna Reddy Seelam,
Dr. A. Manikandan
Catastrophic forgetting remains a significant challenge in continual learning systems, where models
tend to forget previously learned tasks when exposed to new data. This study conducts a comparative
analysis of various mitigation strategies aimed at addressing this issue. We evaluate the effectiveness
of regularization-based approaches (Elastic Weight Consolidation and Learning Without Forgetting),
replay-based approaches (Experience Replay and Generative Replay), architectural modifications
(Progressive Neural Networks and Dynamic Networks), and hybrid methods that combine elements
of the aforementioned strategies. Our experiments use standard benchmark datasets and neural
network models to assess performance based on accuracy on new tasks, retention of old tasks, and
computational cost. Results indicate that while regularization-based methods provide robust retention
of past knowledge with moderate resource requirements, replay-based approaches excel in retaining old
knowledge at the cost of higher computational demands. Architectural methods offer scalable solutions
but with increased complexity and resource usage. Hybrid strategies successfully balance the trade-offs
between retention and new task performance, offering practical solutions for mitigating catastrophic
forgetting. These findings provide valuable insights for selecting appropriate strategies based on specific
application requirements.
