A Comprehensive Review of Continual Learning with Machine Learning Models

Machine learning models have demonstrated exceptional performance in a wide array of individual tasks, and in some instances, they have even surpassed human-level capabilities. Nevertheless, these models grapple with substantial challenges when it comes to achieving continual learning in the face of dynamically incoming data from diverse tasks. Continual learning, which involves consistently acquiring new knowledge while retaining past experiences over extended periods, stands as a pivotal aspect of machine learning systems. Regrettably, continual learning encounters a significant hurdle known as catastrophic forgetting, stemming from the inherent constraints within neural networks, particularly the stability-plasticity dilemma. Catastrophic forgetting manifests as the tendency to disregard previously acquired knowledge when new tasks or domains are introduced, resulting in a pronounced deterioration in performance on tasks or domains learned earlier. To counteract catastrophic forgetting, researchers have devised a multitude of continual learning approaches. In this paper, we aim to provide a comprehensive introduction to the fundamentals of continual learning and present various scenarios where continual learning is applicable. Furthermore, we will meticulously classify and critically evaluate the methodologies put forth in previous research.