Exploring The Role of Transfer Learning In Enhancing Continual Learning Systems
Matta Naresh,
Pothuraju Jangaiah,
Snehal Dileep Guruphale
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.
