  We study the relationship between catastrophic forgetting and properties of
task sequences. In particular, given a sequence of tasks, we would like to
understand which properties of this sequence influence the error rates of
continual learning algorithms trained on the sequence. To this end, we propose
a new procedure that makes use of recent developments in task space modeling as
well as correlation analysis to specify and analyze the properties we are
interested in. As an application, we apply our procedure to study two
properties of a task sequence: (1) total complexity and (2) sequential
heterogeneity. We show that error rates are strongly and positively correlated
to a task sequence's total complexity for some state-of-the-art algorithms. We
also show that, surprisingly, the error rates have no or even negative
correlations in some cases to sequential heterogeneity. Our findings suggest
directions for improving continual learning benchmarks and methods.
