Continual Learning for Lifelong Adaptation

Abstract

Continual learning is a crucial aspect of machine learning, enabling models to adapt to new tasks over time without forgetting previously learned knowledge. This paper presents an overview of continual learning techniques and their applications in enabling lifelong adaptation in machine learning models. We discuss the challenges associated with continual learning, such as catastrophic forgetting and the ability to adapt to new tasks efficiently. We also explore the current state-of-the-art approaches and highlight future research directions in this rapidly evolving field.