  In most machine learning algorithms, training data is assumed to be
independent and identically distributed (iid). When it is not the case, the
algorithm's performances are challenged, leading to the famous phenomenon of
catastrophic forgetting. Algorithms dealing with it are gathered in the
Continual Learning research field. In this paper, we study the regularization
based approaches to continual learning and show that those approaches can not
learn to discriminate classes from different tasks in an elemental continual
benchmark: the class-incremental scenario. We make theoretical reasoning to
prove this shortcoming and illustrate it with examples and experiments.
Moreover, we show that it can have some important consequences on continual
multi-tasks reinforcement learning or in pre-trained models used for continual
learning. We believe that highlighting and understanding the shortcomings of
regularization strategies will help us to use them more efficiently.
