  Continual Learning (CL) is the research field addressing learning without
forgetting when the data distribution is not static. This paper studies
spurious features' influence on continual learning algorithms. We show that
continual learning algorithms solve tasks by selecting features that are not
generalizable. Our experiments highlight that continual learning algorithms
face two related problems: (1) spurious features and (2) local spurious
features. The first one is due to a covariate shift between training and
testing data, while the second is due to the limited access to data at each
training step. We study (1) through a consistent set of continual learning
experiments varying spurious correlation amount and data distribution support.
We show that (2) is a major cause of performance decrease in continual learning
along with catastrophic forgetting. This paper presents a different way of
understanding performance decrease in continual learning by highlighting the
influence of (local) spurious features in algorithms capabilities.
