Hyperparameter Selection in Continual Learning
Thomas L. Lee Sigrid Passano Hellan Linus Ericsson Elliot J. Crowley Amos Storkey
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.