Exploring The Role of Transfer Learning In Enhancing Continual Learning Systems

Abstract

This research explores the integration of transfer learning techniques with continual learning systems to address key challenges in machine learning, such as catastrophic forgetting and task adaptation. Transfer learning methods, including fine-tuning, domain adaptation, and multi-task learning, provide a strong foundation for leveraging pre-existing knowledge across different domains. Continual learning approaches, such as Elastic Weight Consolidation (EWC) and dynamic architectures, focus on maintaining performance on previously learned tasks while acquiring new knowledge. Our experimental results reveal that transfer learning techniques significantly enhance the performance of continual learning systems, with domain adaptation and multi-task learning achieving high accuracy and F1 scores. The integration of transfer learning with continual learning approaches, particularly with EWC and dynamic architectures, demonstrates improved accuracy and reduced forgetting rates. This integrated approach allows for more robust and adaptable machine learning models, capable of efficiently handling a sequence of tasks without compromising previously acquired knowledge. These findings underscore the potential of combining these methodologies to create more resilient and effective learning systems.

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