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.

翻译：在持续学习中，学习器需要按顺序学习多个任务，且每个任务的数据仅获取一次。灾难性遗忘是持续学习面临的主要挑战。为减少遗忘，现有基于回放的方法通过情景记忆存储先前任务样本进行重放。然而，在学习新任务的知识整合过程中，由于新旧知识不平衡，该策略仍会遭受灾难性遗忘。为解决这一问题，我们提出一种名为流形扩张回放（MaER）的新颖回放策略。我们主张，扩张情景记忆中知识表征的隐式流形有助于提升模型的鲁棒性与表达能力。为此，我们提出一种贪婪策略，在内存管理过程中持续增大缓冲区中知识所表征隐式流形的直径。此外，我们引入Wasserstein距离替代交叉熵作为蒸馏损失函数以保留先前知识。通过在MNIST、CIFAR10、CIFAR100和TinyImageNet上的广泛实验验证，我们提出的方法显著提升了持续学习设置下的准确率，超越了当前最先进方法。