Contrastive representation learning has emerged as a promising technique for continual learning as it can learn representations that are robust to catastrophic forgetting and generalize well to unseen future tasks. Previous work in continual learning has addressed forgetting by using previous task data and trained models. Inspired by event models created and updated in the brain, we propose a new mechanism that takes place during task boundaries, i.e., when one task finishes and another starts. By observing the redundancy-inducing ability of contrastive loss on the output of a neural network, our method leverages the first few samples of the new task to identify and retain parameters contributing most to the transfer ability of the neural network, freeing up the remaining parts of the network to learn new features. We evaluate the proposed methods on benchmark computer vision datasets including CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the task-incremental, class-incremental, and domain-incremental continual learning scenarios.

翻译：对比表示学习已成为持续学习领域一种有前景的技术，因为它能学习到对灾难性遗忘具有鲁棒性且能良好泛化至未来未知任务的表示。已有持续学习工作通过利用先前任务数据与训练模型来应对遗忘问题。受大脑中事件模型创建与更新机制的启发，我们提出了一种新的机制，该机制作用于任务边界——即一个任务结束、另一个任务开始之时。通过观察对比损失对神经网络输出的冗余诱导能力，我们的方法利用新任务的前几个样本来识别并保留最有助于神经网络迁移能力的参数，释放网络其余部分以学习新特征。我们在CIFAR10和TinyImagenet等基准计算机视觉数据集上评估了所提方法，并在任务增量、类别增量和领域增量持续学习场景中展现了最先进的性能。