Mitigating Catastrophic Forgetting Through Knowledge Transfer and Weighted Loss Integration in Continual Learning

Continual learning is an emerging field of artificial intelligence (AI) that focuses on equipping models with the ability to adjust to new tasks while retaining previously acquired knowledge. This capability is critical for the development of versatile AI systems. It enables AI to handle dynamic real-world data effectively. Traditional machine learning models often struggle to meet efficiency and resource demands when dealing with changing datasets. Thus, continual learning has become a promising alternative. In this paper, we introduce Knowledge Distillation and Combined Loss Enhanced Continual Learning Network (KDCL), which aims to mitigate catastrophic forgetting and balance the stability and plasticity of continual learning. KDCL combines knowledge distillation and combined loss functions to improve learning efficiency. Through experiments on the CIFAR-100 dataset, KDCL significantly improves the average accuracy compared to existing models, highlighting its capability to retain past knowledge and effectively integrate new information.