  Work on continual learning (CL) has thus far largely focused on the problems
arising from shifts in the data distribution. However, CL can be decomposed
into two sub-problems: (a) shifts in the data distribution, and (b) dealing
with the fact that the data is split into chunks and so only a part of the data
is available to be trained on at any point in time. In this work, we look at
the latter sub-problem, the chunking of data. We show that chunking is an
important part of CL, accounting for around half of the performance drop from
offline learning in our experiments. Furthermore, our results reveal that
current CL algorithms do not address the chunking sub-problem, only performing
as well as plain SGD training when there is no shift in the data distribution.
Therefore, we show that chunking is both an important and currently unaddressed
sub-problem and until it is addressed CL methods will be capped in performance.
Additionally, we analyse why performance drops when learning occurs on
identically distributed chunks of data, and find that forgetting, which is
often seen to be a problem due to distribution shift, still arises and is a
significant problem. We also show that performance on the chunking sub-problem
can be increased and that this performance transfers to the full CL setting,
where there is distribution shift. Hence, we argue that work on chunking can
help advance CL in general.
