A common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned. In this paper, we propose a novel framework called ReCL to slow down forgetting in CL. Our framework exploits an implicit bias of gradient-based neural networks due to which these converge to margin maximization points. Such convergence points allow us to reconstruct old data from previous tasks, which we then combine with the current training data. Our framework is flexible and can be applied on top of existing, state-of-the-art CL methods. We further demonstrate the performance gain from our framework across a large series of experiments, including two challenging CL scenarios (class incremental and domain incremental learning), different datasets (MNIST, CIFAR10, TinyImagenet), and different network architectures. Across all experiments, we find large performance gains through ReCL. To the best of our knowledge, our framework is the first to address catastrophic forgetting by leveraging models in CL as their own memory buffers.

翻译：持续学习（CL）中的一个常见挑战是灾难性遗忘，即在学习了新的附加任务后，旧任务的性能会下降。本文提出了一种名为ReCL的新颖框架，以减缓持续学习中的遗忘。我们的框架利用了基于梯度的神经网络的一种隐式偏差，这种偏差使得网络收敛到间隔最大化点。这些收敛点使我们能够从先前任务中重构旧数据，然后将其与当前训练数据结合。我们的框架具有灵活性，可以应用于现有的最先进的持续学习方法之上。我们通过一系列广泛的实验进一步展示了该框架带来的性能提升，包括两种具有挑战性的持续学习场景（类增量学习和域增量学习）、不同的数据集（MNIST、CIFAR10、TinyImagenet）以及不同的网络架构。在所有实验中，我们发现ReCL带来了显著的性能提升。据我们所知，我们的框架是首个通过利用持续学习中的模型自身作为记忆缓冲区来解决灾难性遗忘问题的方法。