  The ability of a model to learn continually can be empirically assessed in
different continual learning scenarios. Each scenario defines the constraints
and the opportunities of the learning environment. Here, we challenge the
current trend in the continual learning literature to experiment mainly on
class-incremental scenarios, where classes present in one experience are never
revisited. We posit that an excessive focus on this setting may be limiting for
future research on continual learning, since class-incremental scenarios
artificially exacerbate catastrophic forgetting, at the expense of other
important objectives like forward transfer and computational efficiency. In
many real-world environments, in fact, repetition of previously encountered
concepts occurs naturally and contributes to softening the disruption of
previous knowledge. We advocate for a more in-depth study of alternative
continual learning scenarios, in which repetition is integrated by design in
the stream of incoming information. Starting from already existing proposals,
we describe the advantages such class-incremental with repetition scenarios
could offer for a more comprehensive assessment of continual learning models.
