  In the continual learning setting, tasks are encountered sequentially. The
goal is to learn whilst i) avoiding catastrophic forgetting, ii) efficiently
using model capacity, and iii) employing forward and backward transfer
learning. In this paper, we explore how the Variational Continual Learning
(VCL) framework achieves these desiderata on two benchmarks in continual
learning: split MNIST and permuted MNIST. We first report significantly
improved results on what was already a competitive approach. The improvements
are achieved by establishing a new best practice approach to mean-field
variational Bayesian neural networks. We then look at the solutions in detail.
This allows us to obtain an understanding of why VCL performs as it does, and
we compare the solution to what an `ideal' continual learning solution might
be.
