The goal of continual learning is to provide intelligent agents that are capable of learning continually a sequence of tasks using the knowledge obtained from previous tasks while performing well on prior tasks. However, a key challenge in this continual learning paradigm is catastrophic forgetting, namely adapting a model to new tasks often leads to severe performance degradation on prior tasks. Current memory-based approaches show their success in alleviating the catastrophic forgetting problem by replaying examples from past tasks when new tasks are learned. However, these methods are infeasible to transfer the structural knowledge from previous tasks i.e., similarities or dissimilarities between different instances. Furthermore, the learning bias between the current and prior tasks is also an urgent problem that should be solved. In this work, we propose a new method, named Online Continual Learning via the Knowledge Invariant and Spread-out Properties (OCLKISP), in which we constrain the evolution of the embedding features via Knowledge Invariant and Spread-out Properties (KISP). Thus, we can further transfer the inter-instance structural knowledge of previous tasks while alleviating the forgetting due to the learning bias. We empirically evaluate our proposed method on four popular benchmarks for continual learning: Split CIFAR 100, Split SVHN, Split CUB200 and Split Tiny-Image-Net. The experimental results show the efficacy of our proposed method compared to the state-of-the-art continual learning algorithms.

翻译：持续学习的目标是为智能体提供一种能力，使其能够利用从先前任务中获得的知识持续学习一系列任务，同时在先前任务上保持良好性能。然而，该持续学习范式中的一个关键挑战是灾难性遗忘，即当模型适应新任务时，往往会导致先前任务性能严重下降。当前基于记忆的方法通过在学习新任务时重放先前任务的样例，在缓解灾难性遗忘问题上展现出成功。然而，这些方法无法有效迁移先前任务的结构性知识，即不同实例之间的相似性或相异性。此外，当前任务与先前任务之间的学习偏差也是一个亟待解决的问题。本文提出了一种名为"基于知识不变性与分散特性的在线持续学习"（OCLKISP）的新方法，其中我们通过知识不变性与分散特性（KISP）约束嵌入特征的演化。因此，我们能够进一步迁移先前任务的实例间结构性知识，同时缓解因学习偏差导致的遗忘问题。我们在持续学习的四个常用基准数据集（Split CIFAR 100、Split SVHN、Split CUB200和Split Tiny-Image-Net）上对所提方法进行了实证评估。实验结果表明，与最先进的持续学习算法相比，我们提出的方法具有有效性。