  Scenarios in which restrictions in data transfer and storage limit the
possibility to compose a single dataset -- also exploiting different data
sources -- to perform a batch-based training procedure, make the development of
robust models particularly challenging. We hypothesize that the recent
Continual Learning (CL) paradigm may represent an effective solution to enable
incremental training, even through multiple sites. Indeed, a basic assumption
of CL is that once a model has been trained, old data can no longer be used in
successive training iterations and in principle can be deleted. Therefore, in
this paper, we investigate the performance of different Continual Learning
methods in this scenario, simulating a learning model that is updated every
time a new chunk of data, even of variable size, is available. Experimental
results reveal that a particular CL method, namely Learning without Forgetting
(LwF), is one of the best-performing algorithms. Then, we investigate its usage
and parametrization in Morphing Attack Detection and Object Classification
tasks, specifically with respect to the amount of new training data that became
available.
