  Continual learning deals with training models on new tasks and datasets in an
online fashion. One strand of research has used probabilistic regularization
for continual learning, with two of the main approaches in this vein being
Online Elastic Weight Consolidation (Online EWC) and Variational Continual
Learning (VCL). VCL employs variational inference, which in other settings has
been improved empirically by applying likelihood-tempering. We show that
applying this modification to VCL recovers Online EWC as a limiting case,
allowing for interpolation between the two approaches. We term the general
algorithm Generalized VCL (GVCL). In order to mitigate the observed overpruning
effect of VI, we take inspiration from a common multi-task architecture, neural
networks with task-specific FiLM layers, and find that this addition leads to
significant performance gains, specifically for variational methods. In the
small-data regime, GVCL strongly outperforms existing baselines. In larger
datasets, GVCL with FiLM layers outperforms or is competitive with existing
baselines in terms of accuracy, whilst also providing significantly better
calibration.
