Regularization Shortcomings for Continual Learning

In most machine learning algorithms, training data are assumed independent and identically distributed (iid). Otherwise, the algorithms' performances are challenged. A famous phenomenon with non-iid data distribution is known as \say{catastrophic forgetting}. Algorithms dealing with it are gathered in the \textit{Continual Learning} research field. In this article, we study the \textit{regularization} based approaches to continual learning. We show that those approaches can not learn to discriminate classes from different tasks in an elemental continual benchmark: class-incremental setting. We make theoretical reasoning to prove this shortcoming and illustrate it with examples and experiments.