Comparison of Catastrophic Forgetting Mitigation Strategies in Continual Learning Systems

Catastrophic forgetting remains a significant challenge in continual learning systems, where modelstend to forget previously learned tasks when exposed to new data. This study conducts a comparativeanalysis of various mitigation strategies aimed at addressing this issue. We evaluate the effectivenessof 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 elementsof the aforementioned strategies. Our experiments use standard benchmark datasets and neuralnetwork models to assess performance based on accuracy on new tasks, retention of old tasks, andcomputational cost. Results indicate that while regularization-based methods provide robust retentionof past knowledge with moderate resource requirements, replay-based approaches excel in retaining oldknowledge at the cost of higher computational demands. Architectural methods offer scalable solutionsbut with increased complexity and resource usage. Hybrid strategies successfully balance the trade-offsbetween retention and new task performance, offering practical solutions for mitigating catastrophicforgetting. These findings provide valuable insights for selecting appropriate strategies based on specificapplication requirements.