用模型自身当记忆缓冲，缓解持续学习中的遗忘问题
Slowing Down Forgetting in Continual Learning
- 让模型在训练中自动重建旧数据，作为记忆使用
- 在多个数据集和任务上显著提升旧任务性能
- 可适配现有方法，适合研究持续学习的开发者
持续学习中的常见挑战是灾难性遗忘，即学习新任务后旧任务性能下降。本文提出一种名为ReCL的新框架，通过利用梯度神经网络隐含的边际最大化收敛特性，使模型能够重建先前任务的数据，并与当前训练数据结合。该框架灵活，可集成到现有先进持续学习方法之上。我们在大量实验中验证其有效性，涵盖两类挑战性场景（类别增量与领域增量学习）、多个数据集（MNIST、CIFAR10、TinyImageNet）及不同网络架构。所有实验均显示显著性能提升。据我们所知，ReCL是首个通过将持续学习模型本身作为记忆缓冲来应对灾难性遗忘的方法。
原文摘要 · Abstract (English)
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.
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