Continual Learning: Advancing Beyond Sparse Distributed Memory with Distillation and Structure Transfer

The article proposes a novel approach to continual learning using distillation-guided structural transfer, potentially improving performance in dynamic learning environments. This research addresses limitations of existing methods, specifically going beyond sparse distributed memory techniques.

• •Explores a new methodology for continual learning.
• •Utilizes distillation and structural transfer techniques.
• •Addresses limitations of existing methods, potentially improving model robustness.