Continual Learning via Manifold Expansion Replay
Zihao Xu Xuan Tang Yufei Shi Jianfeng Zhang Jian Yang Mingsong Chen Xian Wei
In continual learning, the learner learns multiple tasks in sequence, with
data being acquired only once for each task. Catastrophic forgetting is a major
challenge to continual learning. To reduce forgetting, some existing
rehearsal-based methods use episodic memory to replay samples of previous
tasks. However, in the process of knowledge integration when learning a new
task, this strategy also suffers from catastrophic forgetting due to an
imbalance between old and new knowledge. To address this problem, we propose a
novel replay strategy called Manifold Expansion Replay (MaER). We argue that
expanding the implicit manifold of the knowledge representation in the episodic
memory helps to improve the robustness and expressiveness of the model. To this
end, we propose a greedy strategy to keep increasing the diameter of the
implicit manifold represented by the knowledge in the buffer during memory
management. In addition, we introduce Wasserstein distance instead of cross
entropy as distillation loss to preserve previous knowledge. With extensive
experimental validation on MNIST, CIFAR10, CIFAR100, and TinyImageNet, we show
that the proposed method significantly improves the accuracy in continual
learning setup, outperforming the state of the arts.