Continual Learning Meets Machine Unlearning: Towards Learning and Forgetting on Demand

Neural networks operating in dynamic environments must continually acquire, refine, and apply knowledge over time, a capability known as Continual Learning (CL). Despite its promise, CL faces a central challenge: Catastrophic Forgetting, where learning new information degrades previously acquired knowledge. Traditionally viewed as a limitation, Catastrophic Forgetting can also be leveraged as a mechanism for Machine Unlearning (MU). This emerging paradigm enables the selective removal of learned information from trained models. This paper explores the conceptual and methodological interplay between CL and MU, examining how the principles of incremental learning and beneficial forgetting can coexist and mutually enrich each other. By integrating these two perspectives, we aim to outline new research directions that address both adaptability and data privacy, and to investigate the behavioral and performance implications of deploying CL and MU simultaneously.