We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task". Existing continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM) and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by minimizing the loss for the current task without increasing the loss for previous tasks. However, these methods assume the learner knows when the task changes, which is unrealistic in practice. In this paper, we alleviate the need to provide the algorithm with information about task changes by using an online clustering-based approach on a dynamically updated finite pool of samples or gradients. We thereby successfully counteract catastrophic forgetting in one of the hardest settings, namely: domain-incremental learning, a setting for which the problem was previously unsolved. We showcase the benefits of our approach by applying these ideas to projection-based methods, such as A-GEM and OGD, which lead to task-agnostic versions of them. Experiments on real datasets demonstrate the effectiveness of the proposed strategy and its promising performance compared to state-of-the-art methods.

翻译：我们考虑在持续学习环境中学习多个任务的问题，其中不同任务的数据以流式方式呈现给学习器。该设置的关键挑战是所谓的“灾难性遗忘问题”，即当学习器随后在“新任务”上进行训练时，其在“旧任务”上的性能会下降。现有的持续学习方法，如平均梯度情景记忆（A-GEM）和正交梯度下降（OGD），通过最小化当前任务的损失而不增加先前任务的损失来应对灾难性遗忘。然而，这些方法假设学习器知道任务何时发生变化，这在实践中是不现实的。在本文中，我们通过在动态更新的有限样本或梯度池上采用基于在线聚类的方法，减轻了向算法提供任务变化信息的需要。我们从而成功地在最困难的设置之一——领域增量学习（此前该问题尚未解决）中对抗了灾难性遗忘。通过将这些思想应用于基于投影的方法（如A-GEM和OGD），我们展示了所提方法的优势，并生成了它们的任务无关版本。在真实数据集上的实验证明了所提策略的有效性，以及其与最先进方法相比具有竞争力的性能。