In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the whole data stream with different hyperparameter settings. However, this end-of-training HPO is unrealistic as in practice a learner can only see the stream once. Hence, there is an open question: what HPO framework should a practitioner use for a CL problem in reality? This paper answers this question by evaluating several realistic HPO frameworks. We find that all the HPO frameworks considered, including end-of-training HPO, perform similarly. We therefore advocate using the realistic and most computationally efficient method: fitting the hyperparameters on the first task and then fixing them throughout training.

翻译：在持续学习（CL）中——学习器在数据流上进行训练——标准的超参数优化（HPO）无法应用，因为学习器无法同时获取所有数据。这促使了专门针对CL的HPO框架的开发。调优CL超参数最常用的方法是使用不同的超参数设置对完整数据流进行重复训练。然而，这种训练结束后的HPO是不现实的，因为在实际应用中学习器只能处理一次数据流。因此，存在一个开放性问题：在实践中，从业者应该使用哪种HPO框架来解决CL问题？本文通过评估几种现实的HPO框架来回答这个问题。我们发现，所考虑的所有HPO框架（包括训练结束后的HPO）表现相似。因此，我们主张采用一种现实且计算效率最高的方法：在第一个任务上拟合超参数，然后在整个训练过程中固定这些超参数。