Graph-Based Continual Learning
This work addresses forgetting in continual learning for AI systems, representing an incremental improvement over existing rehearsal methods.
The paper tackles catastrophic forgetting in continual learning by augmenting episodic memory with a learnable random graph to capture sample similarities, resulting in consistent outperformance of baselines on benchmark datasets.
Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an array of independent memory slots. In this work, we propose to augment such an array with a learnable random graph that captures pairwise similarities between its samples, and use it not only to learn new tasks but also to guard against forgetting. Empirical results on several benchmark datasets show that our model consistently outperforms recently proposed baselines for task-free continual learning.