Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unlearning involves selectively forgetting specific subsets of data. In this paper, we introduce a novel framework that jointly tackles both tasks by leveraging controlled knowledge distillation. Our approach enables efficient learning with minimal forgetting and effective targeted unlearning. By incorporating a fixed memory buffer, the system supports learning new concepts while retaining prior knowledge. The distillation process is carefully managed to ensure a balance between acquiring new information and forgetting specific data as needed. Experimental results on benchmark datasets show that our method matches or exceeds the performance of existing approaches in both continual learning and machine unlearning. This unified framework is the first to address both challenges simultaneously, paving the way for adaptable models capable of dynamic learning and forgetting while maintaining strong overall performance.

本研究解决了持续学习和机器遗忘两个重要挑战，并提出了一种利用受控知识蒸馏的统一框架。该方法实现了高效学习、最小遗忘及有效的定向遗忘，实验结果显示其在这两方面的表现超过了现有方法。此框架的提出为具有动态学习和遗忘能力的适应性模型奠定了基础。

持续学习与机器遗忘的统一框架