Graph-Based Continual Learning [article]

Binh Tang, David S. Matteson
2021 arXiv   pre-print
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
more » ... t 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.
arXiv:2007.04813v2 fatcat:gk7hyy5plfgijdjhr7cjfs3sga